机电工程技术2024，Vol.53Issue(7)：46-50,73,6.DOI:10.3969/j.issn.1009-9492.2024.07.009
基于YOLOv5的无人机视角小目标检测算法
UAV Small Target Detection Algorithm Based on YOLOv5
摘要
Abstract
Aiming at the problems of poor detection accuracy and serious missed detection of small targets from the perspective of UAV,a UAV image detection algorithm based on improved YOLOv5 is proposed.Aiming at the problem of small target scale,Spatial Pyramid Pooling(SPP)is replaced by SPPCSPC-GS in the backbone network to enhance the attention ability of dense areas and extract more effective features of small targets.The CBAM attention mechanism is introduced into the neck network to replace the head C3 module with C3CBAM to enhance the context information and improve the spatial and channel feature expression ability.Aiming at the occlusion problem,soft non maximum suppression(Soft NMS)is introduced to improve the detection ability of the model for occlusion and dense targets.The loss function is replaced with EIOU to accelerate convergence and improve positioning effect.The improved model has an average detection accuracy of 42.2% on the VisDrone dataset,which is 10.7% higher than the original YOLOv5s algorithm.The accuracy of small target pedestrians and people with severe occlusion increases by 12% and 13.3%,respectively.Compared with other advanced algorithms,the proposed algorithm performs well and can meet the requirements of UAV perspective image detection tasks.关键词
小目标检测/空间金字塔池化/注意力机制/柔性非极大值抑制/损失函数Key words
small target detection/spatial pyramid pooling/attention mechanism/soft-NMS/loss function分类
信息技术与安全科学引用本文复制引用
宋旭东,查可豪..基于YOLOv5的无人机视角小目标检测算法[J].机电工程技术,2024,53(7):46-50,73,6.基金项目
辽宁省自然科学基金(2019-ZD-0105) （2019-ZD-0105）
