基于改进YOLOv5s的复杂道路交通目标检测算法OA北大核心CSTPCD

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%.