计算机工程与应用2025，Vol.61Issue(14)：112-122,11.DOI:10.3778/j.issn.1002-8331.2412-0303
YOLO-SWR:无人机视角下轻量级交通车辆检测算法
YOLO-SWR:Lightweight Traffic Vehicle Detection Algorithm from UAV Perspective
摘要
Abstract
In intelligent transportation systems,vehicle detection from the perspective of unmanned aerial vehicle(UAV)has attracted more and more attention due to its flexibility and efficiency.Vehicle detection by UAVs faces problems such as small target size,large scale variation,complex background interference,and limited computational resources.How to improve the detection accuracy with limited computing resources is an important challenge.To solve the above problems,this paper proposes YOLO-SWR,a vehicle detection model with higher accuracy and lightweightness.Firstly,the model adds a detection layer for small objects,maintains the three-scale detection to fully extract the location information and detailed features of small objects,and adopts a shared lightweight detection head to reduce the number of network param-eters and computational complexity.Secondly,wavelet pooling is used in the backbone and neck networks instead of tradi-tional convolutions,leveraging both low-frequency and high-frequency components to improve multi-scale feature extraction.Furthermore,the RDS module is integrated into the C3k2 module to strengthen the extraction of features from the scalable receptive field at the high-level of the network,and the shallow features and deep features are fused through the residual structure and depthwise separable convolution,while alleviating the gradient disappearance problem.Finally,the Soft-NMS strategy is used to optimize the bounding box selection process to improve the processing precision of detailed features.Experimental results show that based on the filtered VisDrone2019 dataset,the proposed model YOLO-SWR improves mAP by 9.6 percentage points and reduces the number of parameters by 56%,compared with the base model YOLOv11n.Therefore,the YOLO-SWR model proposed in this paper has significant advantages in both accuracy and lightweightness.关键词
小目标检测/轻量化/交通车辆/目标跟踪/无人机视角Key words
small object detection/lightweight/traffic vehicle/object tracking/UAV perspective分类
交通工程引用本文复制引用
王泉,叶广飞,陈祺东..YOLO-SWR:无人机视角下轻量级交通车辆检测算法[J].计算机工程与应用,2025,61(14):112-122,11.基金项目
道路交通安全公安部重点实验室开放课题基金(2024ZDSYSKFKT01-2). （2024ZDSYSKFKT01-2）
