The superior performance of Transformer in natural language processing has inspired researchers to explore their applications in computer vision tasks. The Transformer-based object detection model, Detection Transformer (DETR), treats object detection as a set prediction problem, introducing the Transformer model to address this task and eliminating the proposal generation and post-processing steps that are typical of traditional methods. The original DETR model encounters issues related to slow training convergence and inefficiency in detecting small objects. To address these challenges, researchers have implemented various improvements to enhance DETR performance. This study conducts an in-depth investigation of both the basic and enhanced modules of DETR, including modifications to the backbone architecture, query design strategies, and improvements to the attention mechanism. Furthermore, it provides a comparative analysis of various detectors and evaluates their performance and network architecture. The potential and application prospects of DETR in computer vision tasks are discussed herein, along with its current limitations and challenges. Finally, this study analyzes and summarizes related models, assesses the advantages and limitations of attention models in the context of object detection, and outlines future research directions in this field.

An instance segmentation algorithm (DE-YOLO) based on the improved YOLOv8 is proposed. To decrease the effect of complex backgrounds in the images, efficient multiscale attention is introduced, and cross-dimensional interaction ensures an even spatial feature distribution within each feature group. In the backbone network, a deformable convolution using DCNv2 is combined with a C2f convolutional layer to overcome the limitations of traditional convolutions and increase flexibility. This is performed to reduce harmful gradient effects and improve the overall accuracy of the detector. The dynamic nonmonotonic Wise-Intersection-over-Union (WIoU) focusing mechanism is employed instead of the traditional Complete Intersection-over-Union (CIoU) loss function to evaluate the quality, optimize detection frame positioning, and improve segmentation accuracy. Meanwhile, Mixup data enhancement processing is enabled to enrich the training features of the dataset and improve the learning ability of the model. The experimental results demonstrate that DE-YOLO improves the mean Average Precision of mask(mAP_mask) and mAP_mask@0.5 by 2.0 and 3.2 percentage points compared with the benchmark model YOLOv8n-seg in the Cityscapes dataset of urban landscapes, respectively. Furthermore, DE-YOLO maintains an excellent detection speed and small parameter quantity while exhibiting improved accuracy, with the model requiring 2.2-31.3 percentage points fewer parameters than similar models.

The objective of image matching is to establish correspondences between similar structures across two or more images. This task is fundamental to computer vision, with applications in robotics, remote sensing, and autonomous driving. With the advancements in deep learning in recent years, Two-Dimensional (2D) image matching algorithms based on deep learning have seen regular improvements in feature extraction, description, and matching. The performance of these algorithms in terms of matching accuracy and robustness has surpassed that of traditional algorithms, leading to significant advancements. First, this study summarizes 2D image matching algorithms based on deep learning features from the past ten years and categorizes them into three types: two-stage image matching based on local features, image matching of joint detection and description, and image matching without feature detection. Second, the study details the development processes, classification methods, and performance evaluation metrics of these three categories and summarizes their advantages and limitations. Typical application scenarios of 2D image matching algorithms are then introduced, and the effects of research progress in 2D image matching on its application domains are analyzed. Finally, the study summarizes the development trends of 2D image matching algorithms and discusses future prospects.

Steel surface defect detection technology in industrial scenarios is hindered by low detection accuracy and slow convergence speed. To address these issues, this study presents an improved YOLOv8 algorithm, namely a YOLOv8n-MDC. First, a Multi-scale Cross-fusion Network (MCN) is added to the backbone network. Establishing closer connections between the feature layers promotes uniform information transmission and reduces semantic information loss during cross-layer feature fusion, thereby enhancing the ability of the model to perceive steel defects. Second, deformable convolution is introduced in the module to adaptively change the shape and position of the convolution kernel, enabling a more flexible capture of the edge features of irregular defects, reducing information loss, and improving detection accuracy. Finally, a Coordinate Attention (CA) mechanism is added to embed position information into channel attention, solving the problem of position information loss and enabling the model to perceive the position and morphological features of defects, thereby enhancing detection precision and stability. Experimental results on the NEU-DET dataset show that the YOLOv8n-MDC algorithm achieves mAP@0.5 of 81.0%, which is 4.2 percentage points higher than that of the original baseline network. The algorithm has a faster convergence speed and higher accuracy; therefore, it meets the requirements of practical industrial production.

The rapid development of Artificial Intelligence (AI) has empowered numerous fields and significantly impacted society, establishing a solid technological foundation for university informatization services. This study explores the historical development of both AI and university informatization by analyzing their respective trajectories and interconnections. Although universities worldwide may focus on different aspects of AI in their digital transformation efforts, they universally demonstrate vast potential of AI in enhancing education quality and streamlining management processes. Thus, this study focuses on five core areas: teaching, learning, administration, assessment, and examination. It comprehensively summarizes typical AI-empowered application cases to demonstrate how AI effectively improves educational quality and management efficiency. In addition, this study highlights the potential challenges associated with AI applications in university informatization, such as data privacy protection, algorithmic bias, and technology dependence. Furthermore, common strategies for addressing these issues such as enhancing data security, optimizing algorithm transparency and fairness, and fostering digital literacy among both teachers and students are elaborated upon in this study. Based on these analyses, the study explores future research directions for AI in university informatization, emphasizing the balance technological innovation and ethical standards. It advocates for the establishment of interdisciplinary collaboration mechanisms to promote the healthy and sustainable development of AI in the field of university informatization.

Federated learning is an emerging distributed learning framework that facilitates the collective engagement of multiple clients in global model training without sharing raw data, thereby effectively safeguarding data privacy. However, traditional federated learning still harbors latent security vulnerabilities that are susceptible to poisoning and inference attacks. Therefore, enhancing the security and model performance of federated learning has become imperative for precisely identifying malicious client behavior by employing gradient noise as a countermeasure to prevent attackers from gaining access to client data through gradient monitoring. This study proposes a robust federated learning framework that combines mechanisms for malicious client detection with Local Differential Privacy (LDP) techniques. The algorithm initially employs gradient similarity to identify and classify potentially malicious clients, thereby minimizing their adverse impact on model training tasks. Subsequently, a dynamic privacy budget based on LDP is designed, to accommodate the sensitivity of different queries and individual privacy requirements, with the objective of achieving a balance between privacy preservation and data quality. Experimental results on the MNIST, CIFAR-10, and Movie Reviews (MR) text classification datasets demonstrate that compared to the three baseline algorithms, this algorithm results in an average 3 percentage points increase in accuracy for sP-type clients, thereby achieving a higher security level with significantly enhanced model performance within the federated learning framework.

Fingerprint recognition is one of the earliest and most mature biometric recognition technologies that is widely used in mobile payments, access control and attendance in the civilian field, and in criminal investigation to retrieve clues from suspects. Recently, deep learning technology has achieved excellent application results in the field of biometric recognition, and provided fingerprint researchers with new methods for automatic processing and the application of fusion features to effectively represent fingerprints, which have excellent application results at all stages of the fingerprint recognition process. This paper outlines the development history and application background of fingerprint recognition, expounds the main processing processes of the three stages of fingerprint recognition, which are image preprocessing, feature extraction, and fingerprint matching, summarizes the application status of deep learning technology in specific links at different stages, and compares the advantages and disadvantages of different deep neural networks in specific links, such as image segmentation, image enhancement, direction field estimation, minutiae extraction, and fingerprint matching. Finally, some of the current problems and challenges in the field of fingerprint recognition are analyzed, and future development directions, such as building public fingerprint datasets, multi-scale fingerprint feature extraction, and training end-to-end fingerprint recognition models, are prospected.

In the context of big data, the rapid advancement of fields such as scientific computing and artificial intelligence, there is an increasing demand for high computational power across various domains. The unique hardware architecture of the Graphics Processing Unit (GPU) makes it suitable for parallel computing. In recent years, the concurrent development of GPUs and fields such as artificial intelligence and scientific computing has enhanced GPU capabilities, leading to the emergence of mature General-Purpose Graphics Processing Units (GPGPUs). Currently, GPGPUs are one of the most important co-processors for Central Processing Units (CPUs). However, the fixed hardware configuration of the GPU after delivery and its limited memory capacity can significantly hinder its performance, particularly when dealing with large datasets. To address this issue, Compute Unified Device Architecture (CUDA) 6.0 introduces unified memory, allowing GPGPU and CPU to share a virtual memory space, thereby simplifying heterogeneous programming and expanding the GPGPU-accessible memory space. Unified memory offers a solution for processing large datasets on GPGPUs and alleviates the constraints of limited GPGPU memory capacity. However, the use of unified memory introduces performance issues. Effective data management within unified memory is the key to enhancing performance. This article provides an overview of the development and application of CUDA unified memory. It covers topics such as the features and evolution of unified memory, its advantages and limitations, its applications in artificial intelligence and big data processing systems, and its prospects. This article provides a valuable reference for future work on applying and optimizing CUDA unified memory.

To reduce social risks caused by the abuse of deepfake technology, an active defense method against deep forgery based on a Generative Adversarial Network (GAN) is proposed. Adversarial samples are created by adding imperceptible perturbation to original images, which significantly distorts the output of multiple forgery models. The proposed model comprises an adversarial sample generation module and an adversarial sample optimization module. The adversarial-sample generation module includes a generator and discriminator. After the generator receives an original image to generate a perturbation, the spatial distribution of the perturbation is constrained through adversarial training. By reducing the visual perception of the perturbation, the authenticity of the adversarial sample is improved. The adversarial sample optimization module comprises basic adversarial watermarking, deep forgery models, and discriminators. This module simulates black-box scenarios to attack multiple deep forgery models, thereby improving the attack and migration of adversarial samples. Training and testing are conducted on commonly used deepfake datasets Celebfaces Attributes (CelebA) and Labeled Faces in the Wild (LFW). Experimental results show that compared with existing active defense methods, the proposed method achieves a defense success rate exceeding 85% based on the cross-model active defense method and generates adversarial samples. Additionally, the method improves efficiency by 20-30 times compared with those of conventional algorithms.

Achieving enhanced detection accuracy is a challenging task in the field of PCB defect detection. To address this problem, this study proposes a series of improvement methods based on PCB defect detection. First, a novel attention mechanism, referred to as BiFormer, is introduced. This mechanism uses dual-layer routing to achieve dynamic sparse attention, thereby reducing the amount of computation required. Second, an innovative upsampling operator called CARAFE is employed. This operator combines semantic and content information for upsampling, thereby making the upsampling process more comprehensive and efficient. Finally, a new loss function based on the MPDIoU metric, referred to as the LMPDIoU loss function, is adopted. This loss function effectively addresses unbalanced categories, small targets, and denseness problems, thereby further improving image detection performance. The experimental results reveal that the model achieves a significant improvement in mean Average Precision (mAP) with a score of 93.91%, 13.12 percentage points higher than that of the original model. In terms of recognition accuracy, the new model reached a score of 90.55%, representing an improvement of 8.74 percentage points. These results show that the introduction of the BiFormer attention mechanism, CARAFE upsampling operator, and LMPDIoU loss function effectively improves the accuracy and efficiency of PCB defect detection. Thus, the proposed methods provide valuable references for research in industrial inspection, laying the foundation for future research and applications.

This study proposes an improved Artificial Potential Field (APF) algorithm (called FC-V-APF) based on Fuzzy Control (FC) and a virtual target point method to solve the local minimum trap and path redundancy issues of the APF method in robot local path planning. First, a virtual target point obstacle avoidance strategy is designed, and the V-APF algorithm is constructed to help the robot overcome local minimum traps by adding an obstacle crossing mechanism and a target point update threshold. Second, a control strategy based on the cumulative angle sum is proposed to assist the robot in exiting a multi-U complex obstacle area. Subsequently, the V-APF and FC algorithms are combined to construct the FC-V-APF algorithm. The corresponding environment is evaluated using real-time data from the radar sensor and designed weight function, and a fuzzy controller is selected to output the auxiliary force to avoid obstacles in advance. Finally, a simulation environment is built on the Robot Operating System (ROS) platform to compare the path planning performance of the FC-V-APF algorithm with that of other algorithms. Considering path length, running time, and speed curves, the designed FC-V-APF algorithm can quickly eliminate traps, reduce redundant paths, improve path smoothness, and reduce planning time.

In recent years, remarkable advancements in Artificial Intelligence (AI) across unimodal domains, such as computer vision and Natural Language Processing (NLP), have highlighted the growing importance and necessity of multimodal learning. Among the emerging techniques, the Zero-Shot Transfer (ZST) method, based on visual-language pre-trained models, has garnered widespread attention from researchers worldwide. Owing to the robust generalization capabilities of pre-trained models, leveraging visual-language pre-trained models not only enhances the accuracy of zero-shot recognition tasks but also addresses certain zero-shot downstream tasks that are beyond the scope of conventional approaches. This review provides an overview of ZST methods based on vision-language pre-trained models. First, it introduces conventional approaches to Few-Shot Learning (FSL) and summarizes its main forms. It then discusses the distinctions between ZST and FSL based on vision-language pre-trained models, highlighting the new tasks that ZST can address. Subsequently, it explores the application of ZST methods in various downstream tasks, including sample recognition, object detection, semantic segmentation, and cross-modal generation. Finally, it analyzes the challenges of current ZST methods based on vision-language pre-trained models and outlines potential future research directions.

Due to low detection accuracy for small targets in complex environments, along with false and missed detections in mainstream traffic sign detection algorithms, an improved algorithm based on YOLOv8s is proposed. This algorithm uses Pconv convolution in the backbone network and incorporates a C2faster module to achieve a lightweight network structure while maintaining network accuracy. In addition, to better utilize the information between low- and high-level features and enhance the regional context association ability, the SPPFCSPC module is designed as a spatial pyramid pooling module based on the concept of SPPF. In addition, by adding the GAM attention mechanism, the feature extraction capability of the network is further enhanced, and the detection accuracy is effectively improved. To improve the detection ability of small targets, a four-fold downsampling branch is added at the neck of the network to optimize target positioning. In addition, the Focal-EIoU loss function is used to replace the original CIoU loss function to accurately define the aspect ratio of the prediction box, which alleviates the problem of imbalance between the positive and negative samples. Experimental results show that on the CCTSDB-2021 traffic sign dataset, the improved algorithm achieved 86.1%, 73.0%, and 81.2% precision, recall, and mAP@0.5, respectively. Compared with the original YOLOv8s algorithm, increases of 0.8%, 6.3%, and 6.9% were observed, respectively. This algorithm significantly reduces false and missed detections in complex weather and harsh environments, offering better overall detection performance than the comparison algorithm, with strong practical value.

With the continuous development of big data and artificial intelligence technologies, knowledge graph embedding is developing rapidly, and knowledge graph applications are becoming increasingly widespread. Knowledge graph embedding improves the efficiency of knowledge representation and reasoning by representing structured knowledge into a low-dimensional vector space. This study provides a comprehensive overview of knowledge graph embedding technology, including its basic concepts, model categories, evaluation indices, and application prospects. First, the basic concepts and background of knowledge graph embedding are introduced, classifying the technology into four main categories: embedding models based on translation mechanisms, semantic- matching mechanisms, neural networks, and additional information. The core ideas, scoring functions, advantages and disadvantages, and application scenarios of the related models are meticulously sorted. Second, common datasets and evaluation indices of knowledge graph embedding are summarized, along with application prospects, such as link prediction and triple classification. The experimental results are analyzed, and downstream tasks, such as question-and-answer systems and recommenders, are introduced. Finally, the knowledge graph embedding technology is reviewed and summarized, outlining its limitations and the primary existing problems while discussing the opportunities and challenges for future knowledge graph embedding along with potential research directions.

Cybersecurity threats are becoming increasingly prevalent with the rapid advancement of Internet technologies. Cyberattacks exhibiting high complexity and diversity, are posing significant challenges to existing defense mechanisms. As an emerging concept, situation awareness technology offers new approaches to enhancing cybersecurity defense. However, the current cybersecurity situation awareness methods suffer from limited data feature extraction capabilities and inadequate handling of long-term sequential data. To address these issues, this study proposes a fusion model that integrates Stack Sparse Auto-Encoder (SSAE), Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and Attention Mechanism (AM). By utilizing SSAE and CNN to extract data features and enhancing the focus on critical information through the AM in the BiGRU model, the proposed model aims to classify the attack categories of abnormal traffic. In conjunction with the network security situational quantification indicators proposed in this study, the network security situation is quantitatively evaluated and classified. The experimental results demonstrate that the proposed fusion model outperforms traditional deep learning models in various metrics, enabling an accurate perception of the network situation.

Pedestrian detection in crowded scenes is a key technology in intelligent monitoring of public space. It enables the intelligent monitoring of crowds, using object detection methods to detect the positions and number of pedestrians in videos. This paper presents Crowd-YOLOv8, an improved version of the YOLOv8 detection model, to address the issue of pedestrians being easily missed owing to occlusion and small target size in densely populated areas. First, nostride-Conv-SPD is introduced into the backbone network to enhance its capability of extracting fine-grained information, such as small object features in images. Second, small object detection heads and the CARAFE upsampling operator are introduced into the neck part of the YOLOv8 network to fuse features at different scales and improve the detection performance in the case of small targets. Experimental results demonstrate that the proposed method achieves an mAP@0.5 of 84.3% and an mAP@0.5∶0.95 of 58.2% on a CrowdedHuman dataset, which is an improvement of 3.7 and 5.2 percentage points, respectively, compared to those of the original YOLOv8n. On the WiderPerson dataset, the proposed method achieves an mAP@0.5 of 88.4% and an mAP@0.5∶0.95 of 67.4%, which is an improvement of 1.1 and 1.5 percentage points compared to those of the original YOLOv8n.

Unmanned Aerial Vehicle (UAV) Multi-Object Tracking (MOT) technology is widely used in various fields such as traffic operation, safety monitoring, and water area inspection. However, existing MOT algorithms are primarily designed for single-UAV MOT scenarios. The perspective of a single-UAV typically has certain limitations, which can lead to tracking failures when objects are occluded, thereby causing ID switching. To address this issue, this paper proposes a Multi-UAV Multi-Object Tracking (MUMTTrack) algorithm. The MUMTTrack network adopts an MOT paradigm based on Tracking By Detection (TBD), utilizing multiple UAVs to track objects simultaneously and compensating for the perspective limitations of a single-UAV. Additionally, to effectively integrate the tracking results from multiple UAVs, an ID assignment strategy and an image matching strategy are designed based on the Speeded Up Robust Feature (SURF) algorithm for MUMTTrack. Finally, the performance of MUMTTrack is compared with that of existing widely used single-UAV MOT algorithms on the MDMT dataset. According to the comparative analysis, MUMTTrack demonstrates significant advantages in terms of MOT performance metrics, such as the Identity F1 (IDF1) value and Multi-Object Tracking Accuracy (MOTA).

Smartphones have become an integral part of modern daily life. The Android operating system currently holds the largest market share in the mobile operating system market owing to its open-source nature and comprehensive ecosystem. Within Android smartphones, the storage subsystem plays a pivotal role, exerting a significant influence on the user experience. However, the design of Android mobile storage systems diverges from server scenarios, necessitating the consideration of distinct factors, such as resource constraints, cost sensitivity, and foreground application prioritization. Extensive research has been conducted in this area. By summarizing and analyzing the current research status in this field, we categorize the issues experienced by users of Android smartphone storage systems into five categories: host-side writing amplification, memory swapping, file system fragmentation, flash device performance, and I/O priority inversion. Subsequently, existing works addressing these five categories of issues are classified, along with commonly used tools for testing and analyzing mobile storage systems. Finally, we conclude by examining existing techniques that ensure the user experience with Android smartphone storage systems and discuss potential avenues for future investigation.

In path planning for mobile robots, challenges arise when dealing with unknown and dynamically changing environments, such as high collision rates with obstacles and susceptibility to local optima. To address these issues, this paper proposes an improved Twin Delayed Deep Deterministic (TD3) algorithm, based on TD3 policy gradient, to enhance the path-planning performance of mobile robots in unknown dynamic environments. First, a Long Short-Term Memory (LSTM) neural network is introduced and combined with the TD3 algorithm. Employing gate structures, historical state information is filtered to perceive the state changes of obstacles within the sensing range for the robot to gain a better understanding of the dynamic environment and movement patterns of obstacles. This enables the mobile robot to accurately predict and respond to the behavior of dynamic obstacles, thereby reducing the collision rate with obstacles. Second, Ornstein-Uhlenbeck(OU) exploration noise is incorporated to facilitate continuous exploration of the surrounding environment, thereby enhancing the robot's random exploration capability. Additionally, a single experience pool is divided into three separate pools-success, failure, and temporary-to improve the sampling efficiency of the effective samples and reduce training time. Finally, simulation experiments are conducted for two different scenarios involving a mixture of dynamic and static obstacles for path planning. A comparative analysis of the experimental results demonstrates that in scenario 1, the proposed algorithm reduces the convergence of the model by 100-200 rounds compared with the Deep Deterministic Policy Gradient (DDPG) and TD3 algorithms. Moreover, it shortens the path length by 0.5-0.8 units and reduces the planning time by 1-4 s. In scenario 2, the proposed algorithm reduces the convergence of the model by 100-300 rounds compared to the TD3 algorithm, shortening the path length by 1-3 units and reducing the planning time by 4-8 s. However, the DDPG algorithm fails as the mobile robot is unable to reach the destination successfully. Therefore, the improved algorithm exhibits superior path planning performance.

Federated learning enables participants to collaboratively model without revealing their raw data, thereby effectively addressing the privacy issue of distributed data. However, as research advances, federated learning continues to face security concerns such as privacy inference attacks and malicious client poisoning attacks. Existing improvements to federated learning mainly focus on either privacy protection or against poisoning attacks without simultaneously addressing both types of attacks. To address both inference and poisoning attacks in federated learning, a privacy-preserving against poisoning federated learning scheme called APFL is proposed. This scheme involves the design of a model detection algorithm that utilizes Differential Privacy (DP) techniques to assign corresponding aggregation weights to each client based on the cosine similarity between the models. Homomorphic encryption techniques are employed for the weighted aggregation of the local models. Experimental evaluations of the MNIST and CIFAR10 datasets demonstrate that APFL effectively filters malicious models and defends against poisoning attacks while ensuring data privacy. When the poisoning ratio is no more than 50%, APFL achieves a model performance consistent with the Federated Averaging (FedAvg) scheme in a non-poisoned environment. Compared with the Krum and FLTrust schemes, APFL exhibits average reductions of 19% and 9% in model test error rate, respectively.

The existing Dung Beetle Optimization(DBO) algorithm has the disadvantages of poor search accuracy and insufficient global search ability, thereby easily falling into local optima. This paper proposes a multi-strategy improved dung beetle optimization algorithm that uses a chaotic opposition-based learning strategy to initialize the dung beetle population, whereby dung beetle individuals are evenly distributed in solution space and population diversity is improved. The golden sine strategy with a nonlinear weight is introduced to improve the ball-rolling behavior and coordinate the global search and local mining ability of the algorithm. Foraging behavior is improved by referring to the position update strategy of the sparrow search algorithm, which brings the population close to the optimal position and improves convergence speed and algorithmic accuracy. Stealing behavior is improved by introducing a piecewise function, which benefits the population in the full global exploration in the early iteration stages, to avoid premature convergence of the algorithm. The Cauchy-Gaussian mutation strategy with a nonlinear weight is used to randomly perturb the current optimal position and guide the algorithm to jump out of the local optimal position. The proposed algorithm is compared with five optimization algorithms using 23 benchmark functions, 12 CEC2022 test functions, and two engineering optimization problems. The experimental results show that the proposed algorithm is superior to the other algorithms and ranks first among at least 21 benchmark functions, 10 CEC2022 test functions, and two engineering optimization problems. Compared with the original dung beetle optimization algorithm, the proposed algorithm exhibits significant improvements in convergence accuracy, convergence speed, global search ability, and stability.

Target detection technology is advancing, but recognizing online listening behavior remains a challenge. Inaccurate identification of online classroom conduct and high model computation owing to limited human supervision and complex target detection models pose problems. To address this, we employed an upgraded YOLOv8-based method to detect and identify online listening behaviors. This approach incorporates a Bidirectional Feature Pyramid Network (BiFPN) to fuse features based on YOLOv8n, thereby enhancing feature extraction and model recognition accuracy. Second, the C3Ghost module is selected over the C2f module on the Head side to minimize the computational burden significantly. The study demonstrates that the YOLOv8n-BiFPN-C3Ghost model achieved an mAP@0.5 score of 98.6% and an mAP@0.5∶0.95 score of 92.6% on an online listening behavior dataset. The proposed model enhanced the accuracy by 4.2% and 5.7%, respectively, compared with other classroom behavior recognition models. Moreover, the required computation amount is only 6.6 GFLOPS, which is 19.5% less than that of the original model. The YOLOv8n-BiFPN-C3Ghost model is capable of detecting and recognizing online listening behavior with greater speed and accuracy while utilizing lower computing costs. This will ultimately enable the dynamic and scientific recognition of online classroom learning among students.

Air pollution is one of the primary challenges in urban environmental governance, with PM_2.5 being a significant contributor that affects air quality. As the traditional time-series prediction models for PM_2.5 often lack seasonal factor analysis and sufficient prediction accuracy, a fusion model based on machine learning, Seasonal Autoregressive Integrated Moving Average (SARIMA)-Support Vector Machine (SVM), is proposed in this paper. The fusion model is a tandem fusion model, which splits the data into linear and nonlinear parts. Based on the Autoregressive Integral Moving Average (ARIMA) model, the SARIMA model adds seasonal factor extraction parameters, to effectively analyze and predict the future linear seasonal trend of PM_2.5 data. Combined with the SVM model, the sliding step size prediction method is used to determine the optimal prediction step size for the residual series, thereby optimizing the residual sequence of the predicted data. The optimal model parameters are further determined through grid search, leading to the long-term predictions of PM_2.5 data and improves overall prediction accuracy. The analysis of the PM_2.5 monitoring data in Wuhan for the past five years shows that prediction accuracy of the fusion model is significantly higher than that of the single model. In the same experimental environment, the accuracy of the fusion model is improved by 99%, 99%, and 98% compared with those of ARIMA, Auto ARIMA, and SARIMA models, respectively and the stability of the model is also better, thus providing a new direction for the prediction of PM_2.5.

This paper proposes a path-planning method based on hybrid A^* and modified RS curve fusion to address the issue of unmanned transfer vehicles in limited scenarios being unable to maintain a safe distance from surrounding obstacles during path planning, resulting in collisions between vehicles and obstacles. First, a distance cost function based on the KD Tree algorithm is proposed and added to the cost function of the hybrid A^* algorithm. Second, the expansion strategy of the hybrid A^* algorithm is changed by dynamically changing the node expansion distance based on the surrounding environment of the vehicle, achieving dynamic node expansion and improving the algorithm's node search efficiency. Finally, the RS curve generation mechanism of the hybrid A^* algorithm is improved to make the straight part of the generated RS curve parallel to the boundary of the surrounding obstacles to meet the requirements of road driving in the plant area. Subsequently, the local path is smoothed to ensure that it meets the continuity of path curvature changes under the conditions of vehicle kinematics constraints to improve the quality of the generated path. The experimental results show that, compared with traditional algorithms, the proposed algorithm reduces the search time by 38.06%, reduces the maximum curvature by 25.2%, and increases the closest distance from the path to the obstacle by 51.3%. Thus, the proposed method effectively improves the quality of path generation of the hybrid A^* algorithm and can operate well in limited scenarios.

In the case of detection tasks in low-light environments, owing to the influence of unfavorable factors, such as low brightness, low contrast, and noise, missed and wrong detections can occur. Hence, a low-light object detection algorithm based on image adaptive enhancement is proposed. Combining conventional image processing methods with deep learning, an image adaptive enhancement network is designed, where multiple adjustable filters are combined in cascade to gradually enhance the input low-light image, and the adjustment parameters of each filter are predicted using a convolutional neural network based on the global information of the input image. The adaptive enhancement network is combined with the YOLOv5 object detection network for end-to-end joint training such that the image enhancement effect is more conducive to object detection. As the low-light object detection process is susceptible to missed detection, the channel attention mechanism SE-Net is improved, and a feature enhancement network is designed and embedded into the end of the Neck region of the YOLOv5 network to reduce the loss of information about potential target features caused by the process of fusion of network features. Experimental results show that the proposed algorithm achieves a detection accuracy of 77.3% on the low-light dataset ExDark, which is 2.1 percentage points higher than that afforded by the original YOLOv5 object detection network, and its detection speed reaches 79 frame/s, which affords real-time detection.

Adversarial examples are crucial for evaluating the robustness of Deep Neural Network (DNN) and revealing their potential security risks. The adversarial example generation method based on a Generative Adversarial Network (GAN), AdvGAN, has made significant progress in generating image adversarial examples; however, the sparsity and amplitude of the perturbation generated by this method are insufficient, resulting in lower authenticity of adversarial examples. To address this issue, this study proposes an improved image adversarial example generation method based on AdvGAN, Squeeze-and-Excitation (SE)-AdvGAN. SE-AdvGAN improves the sparsity of perturbation by constructing an SE attention generator and an SE residual discriminator. The SE attention generator is used to extract the key features of an image and limit the position of perturbation generation. The SE residual discriminator guides the generator to avoid generating irrelevant perturbation. Moreover, a boundary loss based on l₂ norm is added to the loss function of the SE attention generator to limit the amplitude of perturbation, thereby improving the authenticity of adversarial examples. The experimental results indicate that in the white box attack scenario, the SE-AdvGAN method has higher sparsity and smaller amplitude of adversarial example perturbation compared to existing methods and achieves better attack performance on different target models. This indicates that the high-quality adversarial examples generated by SE-AdvGAN can more effectively evaluate the robustness of DNN.

Deep-learning models have achieved impressive results in fields such as image classification; however, they remain vulnerable to interference and threats from adversarial examples. Attackers can craft small perturbations using various attack algorithms to create adversarial examples that are visually indistinguishable yet can lead to misclassification in deep neural networks, posing significant security risks to image classification tasks. To improve the robustness of these models, we propose an adversarial-example defense method that combines adversarial detection and purification using a conditional diffusion model, while preserving the structure and parameters of the target model during detection and purification. This approach features two key modules: adversarial detection and adversarial purification. For adversarial detection, we employ an inconsistency enhancement technique, training an image restoration model that integrates both the high-dimensional features of the target model and basic image features. By comparing the inconsistencies between the initial input and the restored output, adversarial examples can be detected. An end-to-end adversarial purification method is then applied, introducing image artifacts during the denoising process. An adversarial detection and purification module is placed before the target model to ensure its accuracy. Based on detection outcomes, appropriate purification strategies are implemented to remove adversarial examples and improve model robustness. The method was compared with recent adversarial detection and purification approaches on the CIFAR10 and CIFAR100 datasets, using five adversarial attack algorithms to generate adversarial examples. It demonstrated a 5-9 percentage points improvement in detection accuracy over Argos on both datasets in a low-purification setting. Additionally, it exhibited a more stable defense performance than Adaptive Denoising Purification(ADP), with a 1.3 percentage points higher accuracy under Backwards Pass Differentiable Approximation(BPDA) attacks.

Simulators play an indispensable role in an array of scientific fields involving research and development. Particularly in architectural design, simulators provide a secure and cost-effective virtual environment, enabling researchers to conduct rapid experimental analyses and evaluations. Simultaneously, simulators facilitate the acceleration of the chip design and verification processes, thereby conserving time and reducing resource expenditure. However, with the evolutionary advances in processor architectural designs—specifically, the flourishing diversifications featured in dedicated processors—the key role played by simulators in providing substantial feedback for architectural design exploration has gained prominence. This discourse provides an overview of the current developments and applications of architectural simulators, accentuating a few illustrative examples. Analyzing the techniques employed by simulators dedicated to various processors allows for a deeper understanding of the focal points and technical complexities under different architectures. Moreover, this discourse deliberates speculative assessments and critiques of vital aspects of future architectural simulator developments, aspiring to forecast their prospects in the field of processor design research.

Clothes-Changing Person Re-Identification (CC Re-ID) is an emerging research topic in person re-identification, which aims to retrieve pedestrians who have changed their clothes. To date, this task has not been thoroughly studied. Currently, the proposed methods mainly focus on using multi-modal data to assist in decoupling representation learning, such as decoupling the attributes of a pedestrian through auxiliary data such as face, gait, and body contours to reduce the influence of clothing; however, the generalization ability is poor, and additional work is needed to obtain auxiliary information. Furthermore, a method that uses only the original data is insufficient for extracting relevant information, and the performance of the model is poor. To solve the problem of CC Re-ID, a novel multi-branch CC Re-ID method combining feature fusion and channel attention, MBFC, is proposed. This method integrates the channel attention mechanism into the backbone network to learn key information at the feature channel level and designs local and global feature fusion methods to improve the ability of the network to extract fine-grained pedestrian features. In addition, the model adopts a multi-branch structure and uses multiple loss functions, such as clothing counter loss and smooth label cross-entropy loss, to guide the model in learning information unrelated to clothing, reduce the influence of clothing on the model, and thus extract more effective pedestrian information. The proposed model is extensively tested on the PRCC and VC-Clothes datasets. The experimental results indicate that the performance of the proposed model is superior to that of the most advanced CC Re-ID methods in terms of RANK-1 and mean Average Precision (mAP).

Pedestrian detection in intelligent community scenarios needs to accurately recognize pedestrians to address various situations. However, for persons who are occluded or at long distances, existing detectors exhibit problems such as missed detection, detection error, and large models. To address these problems, this paper proposes a pedestrian detection algorithm, Multiscale Efficient-YOLO (ME-YOLO), based on YOLOv8. An efficient feature Extraction Module (EM) is designed to improve network learning and capture pedestrian features, which reduces the number of network parameters and improves detection accuracy. The reconstructed detection head module reintegrates the detection layer to enhance the network's ability to recognize small targets and effectively detect small target pedestrians. A Bidirectional Feature Pyramid Network (BiFPN) is introduced to design a new neck network, namely the Bidirectional Dilated Residual-Feature Pyramid Network (BDR-FPN), and the expanded residual module and weighted attention mechanism expand the receptive field and learn pedestrian features with emphasis, thereby alleviating the problem of network insensitivity to occluded pedestrians. Compared with the original YOLOv8 algorithm, ME-YOLO increases the AP₅₀ by 5.6 percentage points, reduces the number of model parameters by 41%, and compresses the model size by 40% after training and verification based on the CityPersons dataset. ME-YOLO also increases the AP₅₀ by 4.1 percentage points and AP_50∶95 by 1.7 percentage points on the TinyPerson dataset. Moreover, the algorithm significantly reduces the number of model parameters and model size and effectively improves detection accuracy. This method has a considerable application value in intelligent community scenarios.

Blockchain, as a distributed and trusted database, has gained significant attention in academic and industrial circles for its effective application in the domain of digital copyright protection. Traditional digital copyright protection technologies suffer from issues such as difficulties in tracking infringements, complexities in copyright transactions, and inadequate protection of legitimate rights, which severely hampering the development of digital copyright protection endeavors. The immutability, traceability, and decentralization inherent in blockchain technology provide a highly reliable, transparent, and secure solution to mitigate the risks associated with digital copyright infringement. This overview starts with an introduction to the fundamental principles of blockchain technology. Then, it discusses the latest research findings on the integration of blockchain with traditional copyright protection technologies to address the problems inherent in traditional copyright protection schemes. Further, an evaluation of the practical applications and potential of blockchain is conducted, emphasizing its positive impact on the copyright protection ecosystem. Finally, this overview delves into the challenges and future trends related to blockchain based copyright protection, ultimately aiming to establish a more robust and sustainable blockchain copyright protection system.

To address the issues of identification difficulties, low detection accuracy, misdetection, and missing detection of small target vehicles on traffic roads, this study proposes a road traffic small target vehicle detection model, RGGE-YOLOv8, based on the YOLOv8 algorithm with a large kernel and multi-scale gradient combination. First, the RepLayer model replaces the backbone of the YOLOv8 network, and depthwise separable convolution is introduced to expand the context information, thereby enhancing the ability of the model to capture information on small targets. Second, the Complete IoU loss (GIoU) replaces the original loss function to address the issue where the IoU cannot be optimized when there is no overlap. Subsequently, a Global Attention Mechanism (GAM) is introduced to improve the feature representation capability of the network by reducing information loss and enhancing global interactive information. Finally, CSPNet is incorporated, and the gradient combination feature pyramid is parameterized to ensure that the model achieves a large receptive field and high shape deviation. The experimental results indicate that the mAP@0.5 index of the improved algorithm on the Visdrone dataset and the custom dataset reaches 34.8% and 94.7%, respectively. The overall accuracy of the improved algorithm is 2.2 percentage points and 5.51 percentage points higher than that of the original YOLOv8n algorithm. These findings demonstrate the practicability of the RGGE-YOLOv8 model for small target vehicle detection on traffic roads.

Acoustic Scene Classification (ASC) aims to enable computers to simulate the human auditory system in the task of recognizing various acoustic environments, which is a challenging task in the field of computer audition. With rapid advancements in intelligent audio processing technologies and neural network learning algorithms, a series of new algorithms and technologies for ASC have emerged in recent years. To comprehensively present the technological development trajectory and evolution in this field, this review systematically examines both early work and recent developments in ASC, providing a thorough overview of the field. This review first describes application scenarios and the challenges encountered in ASC and then details the mainstream frameworks in ASC, with a focus on the application of deep learning algorithms in this domain. Subsequently, it systematically summarizes frontier explorations, extension tasks, and publicly available datasets in ASC and finally discusses the prospects for future development trends in ASC.

With the development of agricultural information technology, a substantial amount of rice planting-related data has been accumulated on the Internet. To address the challenges that farmers face in quickly obtaining accurate information during the planting process, an intelligent question-answering system is constructed based on a knowledge graph, specifically for rice planting. First, relevant data are obtained through manual collection as well as web crawler technology. Natural language processing techniques, such as the named entity recognition model and an intent recognition model, are built in conjunction with front- and back-end technologies to develop an intelligent question-answering system for rice planting. Experimental results show that in the named entity recognition and intent recognition modules, the F1 values of the constructed models reach 89.17% and 96.54%, respectively, which are higher than those of other conventional models. The intelligent rice planting question-answering system, based on knowledge graph, can accurately answer most inquiries farmers encounter during the process of rice planting, facilitating the management and visualization of rice planting knowledge graph data.

The sequence recommendation algorithm dynamically models the user's historical behavior to predict the content they may be interested in. This study focuses on the application of contrastive Self Supervised Learning (SSL) in sequence recommendation, enhancing the model's representation ability in sparse data scenarios by designing effective self supervised signals. First, a personalized data augmentation method incorporating user preferences is proposed to address the issue of noise introduced by random data augmentation. This method guides the augmentation process based on user ratings and combines different augmentation methods for short and long sequences to generate augmented sequences that align with user preferences. Second, a mixed-augmentation training approach is designed to address the issue of imbalanced feature learning during training. In the early stages of training, augmentation sequences are generated using randomly selected methods to enhance the model performance and generalization. In the later stages, augmentation sequences with high similarity to the original sequences are selected to enable the model to comprehensively learn the actual preferences and behavior patterns of users. Finally, traditional sequence prediction objectives are combined with SSL objectives to infer user representations. Experimental verification is performed using the Beauty, Toys, and Sports datasets. Compared with the best result in the baseline model, the HR@5 indicator of the proposed method increases by 6.61%, 3.11%, and 3.76%, and the NDCG@5 indicator increases by 11.40%, 3.50%, and 2.16%, respectively, for the aforementioned datasets. These experimental results confirm the rationality and validity of the proposed method.

As the main window of human-computer interaction, the mobile phone screen has become an important factor affecting the user experience and the overall performance of the terminal. As a result, there is a growing demand to address defects in mobile phone screens. To meet this demand, in view of the low detection accuracy, high missed detection rate of small target defects, and slow detection speed in the process of defect detection on mobile phone screens, a PGS-YOLO algorithm is proposed, with YOLOv8n as the benchmark model. PGS-YOLO effectively improves the detection ability of small targets by adding a special small target detection head and combining it with the SeaAttention attention module. The backbone and feature fusion networks are integrated into PConv and GhostNetV2 lightweight modules, respectively, to ensure accuracy, reduce the number of model parameters, and improve the speed and efficiency of defect detection. The experimental results show that, in the dataset of mobile phone screen surface defects from Peking University, compared with the results of YOLOv8n, the mAP@0.5 and mAP@0.5∶0.95 of the PGS-YOLO algorithm are increased by 2.5 and 2.2 percentage points, respectively. The algorithm can accurately detect large defects in the process of mobile phone screen defect detection as well as maintain a certain degree of accuracy for small defects. In addition, the detection performance is better than that of most YOLO series algorithms, such as YOLOv5n and YOLOv8s. Simultaneously, the number of parameters is only 2.0×10⁶, which is smaller than that of YOLOv8n, meeting the needs of industrial scenarios for mobile phone screen defect detection.

Traditional vision Simultaneous Localization And Mapping(SLAM) systems are based on the assumption of a static environment. However, real scenes often have dynamic objects, which may lead to decreased accuracy, deterioration of robustness, and even tracking loss in SLAM position estimation and map construction. To address these issues, this study proposes a new semantic SLAM system, named YGL-SLAM, based on ORB -SLAM2. The system first uses a lightweight target detection algorithm named YOLOv8n, to track dynamic objects and obtain their semantic information. Subsequently, both point and line features are extracted from the tracking thread, and the dynamic features are culled based on the acquired semantic information using the Z-score and parapolar geometry algorithms to improve the performance of SLAM in dynamic scenes. Given that lightweight target detection algorithms suffer from missed detection in consecutive frames when tracking dynamic objects, this study designs a detection compensation method based on neighboring frames. Testing on the public datasets TUM and Bonn reveals that YGL-SLAM system improves detection performance by over 90% compared to ORB-SLAM2, while demonstrating superior accuracy and robustness compared to other dynamic SLAM.

Alzheimer's Disease (AD) is an irreversible neurodegenerative disorder that leads to gradual cognitive decline. The evolution of AD symptoms can be long, with subtle changes in biomarkers in brain regions that are detectable by different neuroimaging modalities; however, early detection is challenging. Given the high complexity of neuroimaging data and the irregularity of brain networks, traditional machine learning, and deep neural network models exhibit many shortcomings, and the development of Computer-Aided Diagnostic(CAD) models based on Graph Neural Network (GNN) can be beneficial for probing biomarkers and analyzing neuroimaging patterns in non-Euclidean space. First, a detailed investigation and overview of AD prediction based on GNN classification methods is carried out. Subsequently, an analysis is conducted from the two perspectives of single- and multi-modal data, with a focus on discussing and analyzing the processes of data extraction, brain network modeling, feature learning, and information fusion within the context of single- and multi-modal data applications. A performance evaluation is provided for certain methods. Finally, the primary challenges and future research directions for the application of GNNs in AD diagnosis are outlined to provide beneficial suggestions for further research on AD-assisted diagnosis.

Critical node detection has become an important research domain in complex networks; however, current critical node detection methods suffer from high algorithm time complexity, inaccurate critical node sets obtained, and insufficient consideration of node centrality indicators. Based on this, this study first presents a critical node-detection framework based on layer and node features. This framework introduces a layer-partitioning-based method to enhance the efficiency of selecting the initial coverage set of critical nodes, allowing for the calculation of the initial node set in linear time. Subsequently, the nodes were added back to the original network through node centrality feature indicators until the number of nodes in the solution set met a predefined threshold. To overcome the challenge of local optima during node re-addition, a node centrality index was developed, taking into account the network topology and various node attributes. Experimental results from real networks, after comparing five initial coverage set selection algorithms and five centrality indices, indicate that the proposed method, utilizing layer partitioning and node features, offers more accurate and efficient detection of critical nodes in different network types with enhanced robustness and performance over existing methods.

Convolutional Neural Networks (CNNs) have established their supremacy in the realm of object detection, earning widespread acclaim in scholarly circles for their precision and scalability. This domain has spawned numerous notable models, including the R-CNN series (FastRCNN, FasterRCNN, and others) and the YOLO series. After the success of Transformers in the field of natural language processing, researchers began to explore their application in computer vision, leading to the development of visual backbone networks such as ViT and Swin-ViT. In 2020, the Facebook research team unveiled DETR, an end-to-end object detection algorithm based on Transformers, designed to minimize the need for prior knowledge and post-processing in object detection tasks. Despite the promise shown by DETR in object detection, it is not without its shortcomings, including slow convergence speed, diminished accuracy, and the ambiguous physical significance of target queries. These issues have spurred a wave of research aimed at refining and enhancing the algorithm. This paper endeavors to collate, scrutinize, and synthesize the various efforts directed towards the improvement of DETR, assessing their respective merits and demerits. Furthermore, it offers a comprehensive overview of state-of-the-art research and specialized application domains that employ DETR, and concludes with a prospective analysis of DETR’s future role in the field of computer vision.

The current high-precision vehicle detection model faces challenges due to its excessive parameterization and computational demands, making it unsuitable for efficient operation on intelligent transportation devices. Conversely, lightweight vehicle detection models often sacrifice accuracy, rendering them unsuitable for practical tasks. In response, an improved lightweight vehicle detection network based on YOLOv8 is proposed. This enhancement involves substituting the main network with the FasterNet architecture, which reduces the computational and memory access requirements. Additionally, we replace the Bidirectional Feature Pyramid Network (BiFPN) in the neck with a weighted bidirectional feature pyramid network to simplify the feature fusion process. Simultaneously, we introduce a dynamic detection head with a fusion attention mechanism to achieve nonredundant integration of the detection head and attention. Furthermore, we address the deficiencies of the Complete Intersection over Union (CIoU) in terms of detection accuracy and convergence speed by proposing a regression loss algorithm that incorporates the Scale-invariant Intersection over Union (SIoU) combined with the Normalized Gaussian Wasserstein Distance (NWD). Finally, to minimize the computational demands on edge devices, we implement amplitude-based layer-wise adaptive sparsity pruning, which further compresses the model size. Experimental results demonstrate that the proposed improved model, compared with the original YOLOv8s model, achieves a 1.5 percentage points increase in accuracy, a 78.9% reduction in parameter count, a 67.4% decrease in computational demands, and a 77.8% reduction in model size. This demonstrates the outstanding lightweight effectiveness and practical utility of the proposed model.

Image segmentation is a crucial technology for environmental perception, and it is widely used in various scenarios such as autonomous driving and virtual reality. With the rapid development of technology, computer vision-based blind guiding systems are attracting increasing attention as they outperform traditional solutions in terms of accuracy and stability. The semantic segmentation of road images is an essential feature of a visual guiding system. By analyzing the output of algorithms, the guiding system can understand the current environment and aid blind people in safe navigation, which helps them avoid obstacles, move efficiently, and get the optimal moving path. Visual blind guiding systems are often used in complex environments, which require high running efficiency and segmentation accuracy. However, commonly used high-precision semantic segmentation algorithms are unsuitable for use in blind guiding systems owing to their low running speed and a large number of model parameters. To solve this problem, this paper proposes a lightweight road image segmentation algorithm based on multiscale features. Unlike existing methods, the proposed model contains two feature extraction branches, namely, the Detail Branch and Semantic Branch. The Detail Branch extracts low-level detail information from the image, while the Semantic Branch extracts high-level semantic information. Multiscale features from the two branches are processed and used by the designed feature mapping module, which can further improve the feature modeling performance. Subsequently, a simple and efficient feature fusion module is designed for the fusion of features with different scales to enhance the ability of the model in terms of encoding contextual information by fusing multiscale features. A large amount of road segmentation data suitable for blind guiding scenarios are collected and labeled, and a corresponding dataset is generated. The model is trained and tested on the dataset. The experimental results show that the mean Intersection over Union (mIoU) of the proposed method is 96.5%, which is better than that of existing image segmentation models. The proposed model can achieve a running speed of 201 frames per second on NVIDIA GTX 3090Ti, which is higher than that of existing lightweight image segmentation models. The model can be deployed on NVIDIA AGX Xavier to obtain a running speed of 53 frames per second, which can meet the requirements for practical applications.

Traffic flow prediction has considerable value in design of transportation systems, optimization of road resources, and mitigation of traffic congestion. To address the issue of limited prediction accuracy due to insufficient extraction of temporal periodic features in traffic flow forecasting, in this study, a multi-scale spatio-temporal features soft attention mechanism method MSTFSA is proposed for traffic flow forecasting. The method is based on multi-scale spatial and temporal features and a soft attention mechanism. First, the Graph Talking Head Attention Network (GTHAT) is used to extract the non-Euclidean structural features of the spatial data. The dynamic weights are calculated to represent the impact of traffic flow on adjacent roads at different times. Second, a Bidirectional Enhanced Attention Gated Recurrent Unit (Bi-EAGRU) is utilized to capture the continuity correlation features of temporal data, thereby enhancing the temporal features of each moment and the continuity between adjacent moments. Subsequently, similar traffic flow trends at three scales of periodicity: weekly, daily, and nearest-neighbor time are fused based on soft attention to implement the comprehensive extraction of temporal periodic features. Finally, the prediction accuracy of MSTFSA is verified on the highway datasets PeMS04 and PeMS08. The experimental results demonstrate that MSTFSA provides distinct advantages in terms of traffic flow prediction accuracy. Compared with the baseline methods of Spatio-Temporal Synchronous Graph Convolutional Network (STSGCN) and Attention-based Spatio-Temporal Graph Convolutional Network (ASTGCN), MSTFSA not only reduces the Root Mean Square Error (RMSE) by 7.15% and 3.8% but also decreases the Mean Absolute Error (MAE) by 7.79% and 3.99% on PeMS04 dataset, respectively. In summary, MSTFSA can efficiently extract and merge the multi-temporal and spatial attributes of traffic data, thereby considerably improving the prediction accuracy of traffic flow.

Human pose estimation is widely used in multiple fields, including sports fitness, gesture control, unmanned supermarkets, and entertainment games. However, pose-estimation tasks face several challenges. Considering the current mainstream human pose-estimation networks with large parameters and complex calculations, LitePose, a lightweight pose-estimation network based on a high-resolution network, is proposed. First, Ghost convolution is used to reduce the parameters of the feature extraction network. Second, by using the Decoupled Fully Connected (DFC) attention module, the dependence relationship between pixels in the far distance space position is better captured and the loss in feature extraction due to decrease in parameters is reduced. The accuracy of human pose keypoint regression is improved, and a feature enhancement module is designed to further enhance the features extracted by the backbone network. Finally, a new coordinate decoding method is designed to reduce the error in the heatmap decoding process and improve the accuracy of keypoint regression. LitePose is validated on the human critical point detection datasets COCO and MPII and compared with current mainstream methods. The experimental results show that LitePose loses 0.2% accuracy compared to the baseline network HRNet; however, the number of parameters is less than one-third of the baseline network. LitePose can significantly reduce the number of parameters in the network model while ensuring minimal accuracy loss.

A multimodal remote sensing small-target detection method, BFMYOLO, is proposed to address misdetection and omission issues in remote sensing images with complex backgrounds and less effective information. The method utilizes a pixel-level Red-Green-Blue (RGB) and infrared (IR) image fusion module, namely, the Bimodal Fusion Module (BFM), for effectively making full use of the complementarity of different modes to realize the effective fusion of information from two modalities. In addition, a full-scale adaptive updating module, AA, is introduced to resolve multitarget information conflicts during feature fusion. This module incorporates the CARAFE up-sampling operator and shallow features to enhance non-neighboring layer fusion and improve the spatial information of small targets. An Improved task decoupling Detection Head (IDHead) is designed to handle classification and regression tasks separately, thereby reducing the mutual interference between different tasks and enhancing detection performance by fusing deeper semantic features. The proposed method adopts the Normalized Wasserstein Distance (NWD) loss function as the localization regression loss function to mitigate positional bias sensitivity. Results of experiments on the VEDAI, NWPU VHR-10, and DIOR datasets demonstrate the superior performance of the model, with mean Average Precision when the threshold is set to 0.5 (mAP@0.5) of 78.6%, 95.5%, and 73.3%, respectively. The model thus outperforms other advanced models in remote sensing small-target detection.

To address the problems of low pixel size, complex background, and limited hardware resources in remote sensing image object detection, a small-object detection algorithm that combines Super-Resolution(SR) and feature enhancement is proposed. The Ghost convolution layers in the GhostNet are used to replace the conventional convolution layers, Conv, in the You Only Look Once v8 (YOLOv8) network, reducing the number of parameters and calculations of the network model without compromising detection accuracy. A Super-Resolution Assisted Enhancement (SRAE) is built in the backbone network to improve image resolution and feature extraction capabilities. A Three-layer Feature Fusion (TFF) module is proposed to obtain the spatial features of the lower layer of the backbone network, improve the insufficient feature space extraction in the Spatial Pyramid Pooling Fast(SPPF) layer, and enhance the spatial positioning ability of small targets. A Self-Attention information Transfer (SAT) module is designed to enhance the semantic and global information of small targets while ensuring a lightweight model. The improved model achieves 90.5% Mean Average Precision (mAP)@0.5, 15.1×10⁶ parameter quantity, and 30.3×10⁹ Floating Point Operations Per Second (FLOPs) on the DIOR dataset; additionally, it achieves lightweight while improving detection accuracy compared to other models.

In intelligent situational awareness application scenarios, multi-agent angle tracking problems often occur when moving targets must be monitored and controlled. In contrast to traditional target tracking, the angle tracking task entails not only tracking the spatial coordinates of the target, but also determining the relative angles between targets. Existing control methods often exhibit unstable effects and reduced performance when addressing large-scale problems that are susceptible to environmental changes. To address this problem, the present study proposes a solution scheme based on Multi-Agent Reinforcement Learning(MARL). First, a basic model of the multi-agent angle tracking problem is established, a multi-level simulation decision-making framework is designed, and an adaptive method is proposed for this problem. As a stronger multi-agent reinforcement learning algorithm, AR-MAPPO enhances learning efficiency and model stability by dynamically adjusting the number of data reuse rounds. The experimental results show that the proposed method achieves higher convergence efficiency and better angle tracking performance than traditional methods and other reinforcement learning methods in multi-agent angle tracking tasks.

Considering the problem of low accuracy in existing pedestrian detection methods for dense or small target pedestrians, this study proposes a comprehensive improved algorithm model called YOLOv5_Conv-SPD_DAFPN based on You Only Look Once (YOLO) v5, a non-strided Convolution Space-to-Depth (Conv-SPD), and Double Asymptotic Feature Pyramid Network (DAFPN). First, to address the issue of feature information loss for small targets or dense pedestrians, a Conv-SPD network module is introduced into the backbone network, to replace the original skip convolution, thereby effectively mitigating the problem of feature information loss. Second, to solve the problem of low feature fusion rates caused by nonadjacent feature maps not directly merging, this study proposes DAFPN to significantly improve the accuracy and precision of pedestrian detection. Finally, based on Efficient Intersection over Union (EIoU) and Complete-IoU (CIoU) losses, this study introduces the EfficiCIoU_Loss location loss function to adjust and accelerate the frame regression rate, thereby promoting faster convergence of the network model. The algorithm model improved mAP@0.5 and mAP@0.5∶0.95 by 3.9, 5.3 and 2.1, 2.1 percentage points, respectively, compared to the original YOLOv5 model on the CrowdHuman and WiderPerson pedestrian datasets. After introducing EfficiCIoU_Loss, the model convergence speed improved by 11% and 33%, respectively. These innovative improvements have led to significant progress in dense pedestrian detection based on YOLOv5 in terms of feature information retention, multiscale fusion, and loss function optimization, thereby enhancing performance and efficiency in practical applications.

The complexity of underwater environments and the severe lack of occluded target information, make the extraction of sufficient information difficult, resulting in a high omission factor for underwater occlusion targets. To solve this problem, the present study proposes an occluded underwater target detection algorithm based on an improved adversarial attention mechanism. Using Faster R-CNN as an adversary network, the Adversarial Occlusion sample Generation Network (AOGN), which has a competitive relationship with the Faster R-CNN is designed to improve the detection accuracy for occlusion targets. Through a three-stage learning process, AOGN learns how to generate samples that are difficult for the detection network to classify correctly, thereby improving the detection accuracy of the Faster R-CNN for underwater occlusion targets. Subsequently, the Focal loss function is used to increase the proportion of difficult samples in the loss. Finally, to solve the problem of low resolution of underwater images, SE-ResNet50 is used as the backbone in place of VGG16, thereby enhancing the feature extraction ability. Furthermore, multi-scale feature fusion is adopted based on multi-ROIpooling branches to increase the richness of features. The proposed algorithm achieves mean Average Precision (mAP) values of 73.76% and 86.85% and omission factor values of 2% and 7%, on the URPC and underwater common trash datasets, respectively. These results demonstrate that the algorithm effectively outperforms existing detection methods.

An improved YOLOv7-tiny detection algorithm is proposed to address several challenges, including low efficiency in small-target detection, inaccurate defect localization, excessive parameters in the detection algorithm, and difficulties in deploying the model on terminal equipment for surface defect detection on strip steel. First, GSConv is introduced to replace the standard convolution in the Neck network, followed by the design of an improved and efficient aggregation network, ELAN-G, based on GSConv, which reduces the model parameters while ensuring adequate fusion of strip steel surface defect features. Second, the SPDConv module is integrated between the Head and Neck networks to improve detection of low-resolution and small defects. The module generates an intermediate feature map, which is subsequently filtered and processed to obtain the final feature map, improving the detection accuracy of the Head network for small defects. Finally, the MPDIoU loss function is adopted to leverage the geometric properties of the bounding regression box, simplifying the loss calculation process and enhancing defect localization accuracy. The experimental results indicate that the improved algorithm outperforms six other advanced target detection algorithms on the NEU-DET dataset, demonstrating a more balanced performance. The mean Average Precision (mAP) of the improved algorithm reaches 74.1%, while the parameter count and computational requirements are lower than those of all comparative algorithms, making it suitable for deployment in a steel surface defect detection system within an industrial environment.