Pedestrian Detection Algorithm for Scenic Spots Based on Improved YOLOv8

doi:10.19678/j.issn.1000-3428.0068125

Abstract

Abstract:

The TAPDataset pedestrian detection dataset is used in this study to address the issues of low detection accuracy, large number of algorithm parameters, and limitations of existing public datasets for small target detection in current scenic pedestrian detection. This dataset addresses the deficiencies of existing datasets regarding small target detection. Based on the YOLOv8 algorithm, a new model with high detection accuracy and low hardware requirements, called YOLOv8-L, is proposed. First, the lightweight convolution module DepthSepConv is introduced to reduce the number of parameters and computations of the model. Second, the BiFormer attention mechanism and CARAFE upsampling operator are used to enhance the model's semantic understanding of images and information fusion capability, significantly improving detection accuracy. Finally, a small target detection layer is added to extract more shallow features, effectively improving the model's performance for small target detection. The effectiveness of the algorithm is verified using the TAPDataset, VOC 2007, and TAP+VOC datasets. The experimental results show that compared with YOLOv8, the number of model parameters is reduced by 18.06% on the TAPDataset with unchanged FPS, mAP@0.5 improves by 5.51%, and mAP@0.5∶0.95 improves by 6.03%. On the VOC 2007 dataset, the number of parameters is reduced by 13.6%, with mAP@0.5 improving by 3.96% and mAP@0.5∶0.95 improving by 6.39%. On the TAP+VOC dataset, the number of parameters is reduced by 14.02%, with mAP@0.5 improving by 4.49% and mAP@0.5∶0.95 improving by 5.68%. The improved algorithm demonstrates stronger generalization performance and can be better applied to scenic pedestrian detection tasks.

Key words: intelligent cultural tourism, object detection, attention mechanism, lightweight network, YOLOv8 algorithm

摘要：

针对当前景区行人检测具有检测精度低、算法参数量大和现有公开数据集在小目标检测上存在限制等问题, 创建TAPDataset行人检测数据集, 弥补现有数据集在小目标检测方面的不足, 并基于YOLOv8算法, 构建一种检测精度高、硬件要求低的新模型YOLOv8-L。首先引入DepthSepConv轻量化卷积模块, 降低模型的参数量和计算量。其次采用BiFormer注意力机制和上采样算子CARAFE, 加强模型对图像的语义理解和信息融合能力, 提升模型的检测精度。最后增加一层小目标检测层来提取更多的浅层特征, 从而有效地改善模型对小目标的检测性能。在TAPDataset、VOC 2007及TAP+VOC数据集上的实验结果表明, 与YOLOv8相比, 在FPS基本不变的情况下, 在TAPDataset数据集上, 模型的参数量减少了18.06%, mAP@0.5提高了5.51%, mAP@0.5∶0.95提高了6.03%;在VOC 2007数据集上, 模型的参数量减少了13.6%, mAP@0.5提高了3.96%, mAP@0.5∶0.95提高了6.39%;在TAP+VOC数据集上, 模型的参数量减少了14.02%, mAP@0.5提高了4.49%, mAP@0.5∶0.95提高了5.68%。改进算法具有更强的泛化性能, 能够更好地适用于景区行人检测任务。

关键词: 智慧文旅, 目标检测, 注意力机制, 轻量化网络, YOLOv8算法

Xiangquan GUI, Shiqing LIU, Li LI, Qingsong QIN, Tangyan LI. Pedestrian Detection Algorithm for Scenic Spots Based on Improved YOLOv8[J]. Computer Engineering, 2024, 50(7): 342-351.

贵向泉, 刘世清, 李立, 秦庆松, 李唐艳. 基于改进YOLOv8的景区行人检测算法[J]. 计算机工程, 2024, 50(7): 342-351.

/ Recommend / Download Citations

URL: https://www.ecice06.com/EN/10.19678/j.issn.1000-3428.0068125

https://www.ecice06.com/EN/Y2024/V50/I7/342

Figures/Tables 19

Fig.1 YOLOv8 architecture

Fig.2 Conv module structure

Fig.3 C2f module structure

Fig.4 SPPF structure

Fig.5 YOLOv8-L architecture

Fig.6 Depthwise convolution

Fig.7 Pointwise convolution

Fig.8 BRA structure

Fig.9 BiFormer structure

Fig.10 Effest comparisons of YOLOv5、YOLOv8 and YOLOv8-L

References 25

1	TANG C, HOU C P. RGBD salient object detection by structured low-rank matrix recovery and Laplacian constraint. Transactions of Tianjin University, 2017, 23(2): 176- 183. doi: 10.1007/s12209-017-0032-7
2	王亮, 张超. 一种基于YOLOv5的轻量型行人检测方法. 工业控制计算机, 2023, 36(4): 84-86, 89. URL
	WANG L, ZHANG C. A lightweight pedestrian detection method based on YOLOv5. Industrial Control Computer, 2023, 36(4): 84-86, 89. URL
3	ZOU Z X, CHEN K Y, SHI Z W, et al. Object detection in 20 years: a survey[EB/OL]. [2023-06-10]. https://levir.buaa.edu.cn/publications/od_survey.pdf.
4	洪松, 高定国. 基于YOLOv3的车辆和行人检测方法. 电脑知识与技术, 2020, 16(8): 192-193, 198. URL
	HONG S, GAO D G. Vehicle and pedestrian detection method based on YOLOv3. Computer Knowledge and Technology, 2020, 16(8): 192-193, 198. URL
5	HONG W W, MA Z H, et al. Detection of green asparagus in complex environments based on the improved YOLOv5 algorithm. Sensors, 2023, 23, 1562. doi: 10.3390/s23031562
6	WANG Q, FENG W Q, YAO L F, et al. TPH-YOLOv5-air: airport confusing object detection via adaptively spatial feature fusion. Remote Sensing, 2023, 15(15): 3883. doi: 10.3390/rs15153883
7	TERVEN J, CORDOVA-ESPARZA D. A comprehensive review of YOLO: from YOLOv1 to YOLOv8 and beyond[EB/OL]. [2023-06-10]. https://arxiv.org/pdf/2304.00501.pdf.
8	邱天衡, 王玲, 王鹏, 等. 基于改进YOLOv5的目标检测算法研究. 计算机工程与应用, 2022, 58(13): 63- 73. URL
	QIU T H, WANG L, WANG P, et al. Research on object detection algorithm based on improved YOLOv5. Computer Engineering and Applications, 2022, 58(13): 63- 73. URL
9	孙传猛, 王燕平, 王冲, 等. 融合改进YOLOv3与三次样条插值的煤岩界面识别方法. 采矿与岩层控制工程学报, 2022, 4(1): 81- 90. URL
	SUN C M, WANG Y P, WANG C, et al. Coal-rock interface identification method based on improved YOLOv3 and cubic spline interpolation. Journal of Mining and Strata Control Engineering, 2022, 4(1): 81- 90. URL
10	王程, 刘元盛, 刘圣杰. 基于改进YOLOv4的小目标行人检测算法. 计算机工程, 2023, 49(2): 296-302, 313. URL
	WANG C, LIU Y S, LIU S J. Small-target pedestrian-detection algorithm based on improved YOLOv4. Computer Engineering, 2023, 49(2): 296-302, 313. URL
11	严开忠, 马国梁, 许立松, 等. 基于改进YOLOv3的机载平台目标检测算法. 电光与控制, 2021, 28(5): 70- 74. URL
	YAN K Z, MA G L, XU L S, et al. Improved YOLOv3 based target detection algorithm for airborne platform. Electronics Optics & Control, 2021, 28(5): 70- 74. URL
12	王艺成, 张国良, 张自杰. 基于改进YOLOv5的小目标检测方法. 计算机与现代化, 2023,(5): 100- 105. URL
	WANG Y C, ZHANG G L, ZHANG Z J. Small object detection method based on improved YOLOv5. Computer and Modernization, 2023,(5): 100- 105. URL
13	李文豪, 周斌, 胡波, 等. 基于轻量化网络的遮挡人脸检测. 中南民族大学学报(自然科学版), 2022, 41(3): 339- 346. URL
	LI W H, ZHOU B, HU B, et al. Occlusion face detection based on lightweight network. Journal of South-Central Minzu University (Natural Science Edition), 2022, 41(3): 339- 346. URL
14	李闻, 李小春, 闫昊雷. 基于改进YOLO v3的PCB缺陷检测. 电光与控制, 2022, 29(4): 106- 111. URL
	LI W, LI X C, YAN H L. PCB defect detection based on improved YOLO v3. Electronics Optics & Control, 2022, 29(4): 106- 111. URL
15	CUI C, GAO T Q, WEI S Y, et al. PP-LCNet: a lightweight CPU convolutional neural network[EB/OL]. [2023-06-10]. http://arxiv.org/abs/2109.15099v1.
16	ZHAN Y, YU J. Multi-task compositional network for visual relationship detection. International Journal of Computer Vision, 2020, 128(8): 2146- 2165.
17	ZHU L, WANG X J, KE Z G, et al. BiFormer: vision transformer with bi-level routing attention[EB/OL]. [2023-06-10]. https://arxiv.org/abs/2303.08810.
18	杨永波, 李栋. 改进YOLOv5的轻量级安全帽佩戴检测算法. 计算机工程与应用, 2022, 58(9): 201- 207. URL
	YANG Y B, LI D. Lightweight helmet wearing detection algorithm of improved YOLOv5. Computer Engineering and Applications, 2022, 58(9): 201- 207. URL
19	WANG J Q, CHEN K, XU R, et al. CARAFE: content-aware ReAssembly of FEatures[C]//Proceedings of IEEE/CVF International Conference on Computer Vision. Washington D. C., USA: IEEE Press, 2019: 458-467.
20	WU D L, JIANG S, ZHAO E L, et al. Detection of camellia oleifera fruit in complex scenes by using YOLOv7 and data augmentation. Applied Sciences, 2022, 12(22): 11318.
21	ABOAH A, WANG B, BAGCI U, et al. Real-time multi-class helmet violation detection using few-shot data sampling technique and YOLOv8[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2023: 5350-5358.
22	GEOFFERY A. Real-time helmet violation detection using YOLOv5 and ensemble learning[EB/OL]. [2023-06-10]. https://arxiv.org/pdf/2304.09246.
23	LAWAL O M. YOLOv5-LiNet: a lightweight network for fruits instance segmentation. PLoS One, 2023, 18(3): e0282297.
24	KUMAR S, KUMAR A, KUMAR K. GhostNet-YOLO algorithm for object detection in UAV image[C]//Proceedings of the 7th International Conference on Image Information Processing. Washington D. C., USA: IEEE Press, 2023: 293-299.
25	WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2023: 2781-2795.

[1]	LI Junjun, DONG Jiangang, LI Kun. Research on Kubernetes-based Cluster Energy-Saving Strategy [J]. Computer Engineering, 2024, 50(9): 82-91.
[2]	LIN Chang, GUO Wei, REN Zhecong, JIN Haibo. Unification Algorithm for Object Tracking and Segmentation Based on Transformer [J]. Computer Engineering, 2024, 50(9): 130-141.
[3]	LI Zelin, LÜ Zhaofeng, CHEN Fuqiang, LI Ke. Entity Alignment Model Based on Multi-Hop Information Fusion [J]. Computer Engineering, 2024, 50(9): 142-152.
[4]	WANG Ruying, MA Jiajun, DONG Jianqiang, LIU Wanlong, ZHANG Haitao, YIN Kai, ZHAO Bochao. Industrial Load Forecasting Method Based on MTS-BiGRU-DMHSA [J]. Computer Engineering, 2024, 50(9): 169-178.
[5]	CAI Junmin, LIANG Zhengyou, SUN Yu, CHEN Ziao. Research on Lightweight Point Cloud Classification Based on Deformable 3D Graph Convolution [J]. Computer Engineering, 2024, 50(9): 255-265.
[6]	ZHU Kai, LI Li, ZHANG Tong, JIANG Sheng, BIE Yiming. Multi-Stage Motion Blur Image Restoration Network Based on Transformer [J]. Computer Engineering, 2024, 50(9): 276-285.
[7]	ZHANG Tianpeng, HAN Jing, LÜ Xueqiang. Super-Resolution-Aided Small-Target Detection Based on Multi-Task Learning [J]. Computer Engineering, 2024, 50(9): 304-312.
[8]	GUO Min, ZHANG Xihan, LI Yang. Integrated Attentional Teacher Mutual Consistency Semi-Supervised Medical Image Segmentation [J]. Computer Engineering, 2024, 50(9): 313-323.
[9]	ZENG Yuqi, LIU Bo, ZHONG Baichang, ZHONG Jin. Student Classroom Behavior Detection Algorithm Based on Improved YOLOv8 in Smart Education [J]. Computer Engineering, 2024, 50(9): 344-355.
[10]	Suzhe WANG, Xueying ZHANG, Xiaoyu CHEN, Fenglian LI, Zeling WU. EEG Enhancement Algorithm Based on Combination of Effective Attention and GAN [J]. Computer Engineering, 2024, 50(8): 336-344.
[11]	Yu WANG, Qi QI, Chun WANG, Cai XU. High-Precision Fault Diagnosis Method for Energy Storage Inverter Signals [J]. Computer Engineering, 2024, 50(8): 389-396.
[12]	Rixin RAO, Yiwen WANG, Lizhi ZENG, Xintian TONG, Haitao ZHAO. Lightweight Network Model for Waste Cable Detection [J]. Computer Engineering, 2024, 50(8): 22-30.
[13]	Huayu LI, Zhikang ZHANG, Yang YAN, Yang YUE. Enhanced Domain Multi-modal Entity Recognition Based on Knowledge Graph [J]. Computer Engineering, 2024, 50(8): 31-39.
[14]	Lei WANG, Shipeng DANG, Feng PAN. Model for Predicting Concealed Accessory Pathway Based on Convolutional Neural Network [J]. Computer Engineering, 2024, 50(8): 40-49.
[15]	Han CHEN, Chunlei ZHAO, Haoda JIANG, Chundong WANG. Research on App User Intent Recognition Based on Fusion Model and Semantic Network [J]. Computer Engineering, 2024, 50(8): 50-63.

Please choose a citation manager

Content to export