计算机工程与应用2024，Vol.60Issue(2)：191-199,9.DOI:10.3778/j.issn.1002-8331.2307-0223
改进YOLOv5s的无人机视角下小目标检测算法
Improved YOLOv5s Small Object Detection Algorithm in UAV View
摘要
Abstract
Aiming at the problems such as the long distance between UAV and object in flight,the obvious difference in the size of the photographed object and the existence of object occlusion,an improved algorithm BD-YOLO based on YOLOv5s for small object detection under UAV perspective is proposed.In the feature fusion network,bi-level routing attention(BRA)is used to filter the least relevant features in the feature map in a dynamic sparse way,and retain some important regional features,so as to improve the feature extraction ability of the model.Since the feature map will lose a lot of location and feature information after multiple subsampled,a dynamic object detection head(DyHead)combining attention mechanism is adopted.The DyHead integrates scale perception,space perception and task perception to achieve stronger feature representation capability.Focal-EIoU Loss function is used to solve the problem of inaccurate regression results of CIoU Loss calculation in YOLOv5s,so as to improve the detection accuracy of the model for small object.The experimental results show that on the VisDrone2019-DET dataset,the BD-YOLO model has increased the mean average precision(mAP)index by 0.062 compared with the YOLOv5s model,and has better results for small object detection than other mainstream models.关键词
无人机视角/YOLOv5s/小目标/注意力机制/损失函数Key words
unmanned aerial vehicle perspective/YOLOv5s/small object/attention mechanism/loss function分类
信息技术与安全科学引用本文复制引用
吴明杰,云利军,陈载清,钟天泽..改进YOLOv5s的无人机视角下小目标检测算法[J].计算机工程与应用,2024,60(2):191-199,9.基金项目
云南省教育厅科学研究基金(2023Y0533). （2023Y0533）
