郑州大学学报（工学版）2024，Vol.45Issue(3)：64-71,8.DOI:10.13705/j.issn.1671-6833.2024.03.016
基于改进YOLOv5s的复杂道路交通目标检测算法
Complex Road Traffic Target Detection Algorithm Based on Improved YOLOv5s
摘要
Abstract
A complex road traffic object detection algorithm was proposed to address the issue of traffic target detec-tion algorithms' inability to resist complex background interference and insufficient detection performance in the cur-rent autonomous driving scenario.At first,the multi-head self-attention residual module(MHSARM)was used to improve the feature information of the target to be inspected while decreasing the complex background interference.Secondly,in the feature fusion area,CoordConv was used instead of traditional Conv,so that the network could perceive spatial information and improve network detection accuracy.The improved YOLOv5s algorithm had stron-ger feature extraction ability and good generalisation ability in complex roads,and mAP_0.5 reached 93.3%and 47.4%,respectively,which was higher than that of YOLOv5s 0.9%and 1.4%.In addition,compared with the latest target detection algorithms YOLOv7 and YOLOv8,the mAP_0.5 of improved YOLOv5s improved by 1.3%and 2.2%,respectively.Compared with the latest research results of Sim-YOLOv4 algorithm on Kitti dataset,mAP_0.5 improved 2.2%.关键词
自动驾驶/目标检测/YOLOv5s/MHSARM/CoordConvKey words
automatic driving/target detection/YOLOv5s/MHSARM/CoordConv分类
信息技术与安全科学引用本文复制引用
汤林东,云利军,罗瑞林,卢琳..基于改进YOLOv5s的复杂道路交通目标检测算法[J].郑州大学学报（工学版）,2024,45(3):64-71,8.基金项目
国家自然科学基金资助项目(62265017) （62265017）
