江西科学2024，Vol.42Issue(2)：378-387,10.DOI:10.13990/j.issn1001-3679.2024.02.025
基于YOLOv5s改进的无人机航拍图像车辆检测模型
Improved UAV Aerial Image Vehicle Detection Model Based on YOLOv5s
摘要
Abstract
Aiming at the problems of serious vehicle occlusion,many small-scale targets,complex background information,and serious false detection and missed detection in UAV aerial image vehi-cle detection tasks,this paper proposes a vehicle target detection model based on YOLOv5.Firstly,a small target feature detection layer is added to enhance the complex extraction of effective location feature information in the shallow feature map,so as to alleviate the problem of lack of dense small target feature information caused by deep convolution.Secondly,GSConv convolution and VOVGSC-SP modules are used in Neck to lighten the model and improve the detection accuracy.Thirdly,Mi-sh is used as the global activation function to improve the propagation and expression ability of fea-ture information in the deep network.Then,for the model's positioning accuracy of the detection target,EIoU is used as the regression box to locate the loss.Finally,the Transformer module is in-troduced in Backbone to enhance the model receptive field,improve the extraction ability of key point information,and enhance the anti-interference ability of the model.Experimental results show that the average detection accuracy(mAP)of the final improved model reaches 83.8%,which is 5.5%higher than that of the original YOLOv5s model,and the detection accuracy of small targets is significantly improved.关键词
深度学习/卷积神经网络/车辆检测/YOLOv5/损失函数/TransformerKey words
in-depth learning/convolutional neural networks/vehicle inspection/YOLOv5/loss func-tion/Transformer分类
信息技术与安全科学引用本文复制引用
张立亭,刘丞丰,罗亦泳,邓先金,张紫怡..基于YOLOv5s改进的无人机航拍图像车辆检测模型[J].江西科学,2024,42(2):378-387,10.基金项目
江西省自然科学基金青年资助项目(20224BAB213037) （20224BAB213037）
江西省教育厅科学技术研究项目(GJJ2200745) （GJJ2200745）
江西省哲学社会科学基地、江西省软科学培育基地联合项目(22SJDJC02) （22SJDJC02）
东华理工大学博士启动资助项目(DHBK2022001). （DHBK2022001）
