Abstract: With the continuous progress in drone technology, the detection of small targets in drone aerial photography has garnered increasing scholarly attention. To tackle the challenges of low detection accuracy and high missed detection rates caused by small target scales, dense distributions, and complex backgrounds in drone aerial images, we propose PK-YOLO, a drone aerial small target detection model based on an improved YOLOv11n. Using YOLOv11n as the baseline model, we introduce targeted enhancements in three key areas: feature extraction, feature fusion, and loss function, with the objective of improving the detection accuracy and overall robustness of small targets in drone aerial scenarios. In the feature extraction phase, recognizing that YOLOv11n's feature pyramid structure, spanning layers P3 to P5, exhibits limited responsiveness to extremely small targets (less than 32×32 pixels), leading to missed and false detections, we incorporate an additional small target detection layer, namely the P2 detection layer. This layer leverages higher-resolution feature maps to preserve richer spatial position and edge detail information, facilitating a gradual fusion from high-resolution shallow features to semantically rich deep features, thereby significantly enhancing the network's capacity for extracting features from small targets. Furthermore, we design a C3k2_KSFA module that integrates the Kernel Selective Fusion Attention (KSFA) mechanism to replace the original C3k2 module in the backbone network. The KSFA mechanism draws inspiration from dynamic convolution and selective kernel networks, extracting multi-scale features by employing convolution kernels with various dilation rates and sizes in parallel. It then adaptively assigns optimal kernel weights to different spatial locations using a spatial-spectral selection mechanism, enabling the model to dynamically adjust its receptive field based on the actual size of the input target, thus improving feature representation and discrimination for targets of varying scales. During the feature fusion stage, traditional fixed upsampling methods struggle to accommodate the dramatic variations in target scales in drone images, often resulting in the loss of small target features and susceptibility to complex background interference. To address this, we replace the fixed upsampling module in the neck network with DySample dynamic upsampling. Utilizing a point sampling strategy, DySample adaptively adjusts sampling point positions according to the feature map content, effectively suppressing background interference while enlarging the feature map and reducing the loss rate of small target features. In terms of loss function optimization, acknowledging that the CIoU loss function employed by YOLOv11n is prone to interference from low-quality prediction boxes, leading to subpar localization accuracy, we introduce the Inner-IoU mechanism to enhance Wise-IoU v3, proposing the Inner-WIoU loss function. This function improves the fine-grained description of target location and shape while retaining the ability to focus on challenging samples through a dynamic non-monotonic frequency modulation mechanism and auxiliary bounding boxes, thereby effectively enhancing the localization accuracy of small targets. To validate the model's effectiveness, we conduct comparative experiments on three publicly available datasets: VisDrone2019, TinyPerson, and RSOD. The experimental results on the VisDrone2019 dataset reveal that, compared to the baseline model, the PKD-YOLO model achieves improvements of 5.0% and 3.3% in mAP@0.5 and mAP@0.5:0.95, respectively, along with a 5.0% increase in precision. On the more demanding TinyPerson dataset, these three metrics improve by 5.7%, 1.9%, and 7.4%, respectively, fully demonstrating the model's superior detection capabilities for small targets in complex environments. On the RSOD dataset, mAP@0.5 and mAP@0.5:0.95 reach 96.1% and 68.8%, respectively, representing improvements of 2.7% and 3.6% over the baseline model, indicating that the model maintains high localization accuracy while enhancing its ability to discriminate target detection confidence. Combining the experimental results from these three datasets, PKD-YOLO exhibits exceptional small target detection performance across various drone aerial scenarios, confirming the effectiveness and generalizability of our proposed improvement methods.

摘要： 随着无人机技术的不断进步，无人机航拍小目标检测受到越来越广泛的关注。针对无人机航拍图像中目标尺度小、分布密集及背景复杂导致的检测精度低、漏检率高等问题，提出一种基于改进YOLOv11n的无人机航拍小目标检测模型PK-YOLO。以YOLOv11n为基线模型，分别从特征提取、特征融合与损失函数三个方面对模型进行针对性改进，旨在提升无人机航拍场景下小目标的检测精度与整体鲁棒性。在特征提取阶段，针对YOLOv11n采用P3至P5层的特征金字塔结构，对尺寸小于32×32像素的极小目标响应能力有限，导致的漏检、误检等问题，研究新增小目标检测层P2检测层，利用更高分辨率的特征图保留更丰富的空间位置信息与边缘细节信息，实现从高分辨率浅层特征到强语义深层特征的渐进式融合，显著增强网络对微小目标的特征提取能力。其次，设计融合核选择融合注意力机制（Kernel Selective Fusion Attention, KSFA）的C3k2_KSFA模块，替换原始骨干网络中的C3k2模块。KSFA机制借鉴动态卷积与选择性核网络的思想，通过并行使用多种扩张率与尺寸的卷积核提取多尺度特征，并利用空间-光谱选择机制自适应地为不同空间位置分配最优的核权重，使模型能够根据输入目标的实际尺寸灵活调整感受野，从而提升对不同尺度目标的特征表达与区分能力。在特征融合阶段，传统固定上采样操作难以适配无人机图像中目标尺度剧烈变化的特性，容易造成小目标特征丢失且易受复杂背景干扰。为此，研究将颈部网络中的固定上采样模块替换为DySample动态上采样。DySample基于点采样策略，能够根据特征图内容自适应调整采样点位置，在放大特征图的同时有效抑制背景干扰，降低小目标特征的丢失率。在损失函数优化方面，针对YOLOv11n所采用的CIoU损失函数易受低质量预测框干扰而导致定位精度不足的问题，引入Inner-IoU机制改进Wise-IoU v3，提出Inner-WIoU损失函数。该函数通过动态非单调调频机制与辅助边框，在保留对困难样本关注能力的同时，增强对目标位置与形状的精细描述能力，有效提升了小目标的定位精度。为验证模型有效性，本文在VisDrone2019、TinyPerson及RSOD三个公开数据集上开展对比实验。在VisDrone2019数据集上的实验结果表明，与基线模型相比，PKD-YOLO模型在mAP@0.5和mAP@0.5:0.95上分别提升5.0％和3.3％，精确率提升5.0％；在更具挑战性的TinyPerson数据集上，上述三项指标分别提升5.7％、1.9％与7.4％，充分证明了模型在复杂场景下对小目标的检测优势。在RSOD数据集上，mAP@0.5与mAP@0.5:0.95分别达到了96.1％与68.8％，较基线模型分别提升2.7％与3.6％，表明模型在保持较高定位精度的同时，对目标检测的置信度判别能力有所增强。综合三个数据集的实验结果，PKD YOLO在多种无人机航拍场景下均表现出优越的小目标检测性能，验证了本文所提改进方法的有效性与泛化能力。