Traffic Sign Detection Method Based on Improved YOLOv5s
LUO Hao
LI Xuebing
GU Yujuan
Abstract：Aiming at the problems of low recognition and detection ability of occluded objects,low accuracy and difficult rec-ognition of traffic signs of distant small targets in the detection of traffic signs by YOLOv5s model,a detection model based on im-proved YOLOv5s traffic signs is proposed.Firstly,by integrating the convolutional attention module(CBAM)and the weighted bi-directional feature pyramid network(BiFPN),the detection capability of YOLOv5s network model for traffic signs is strength-ened.Secondly,small target detection layer is added to splice shallow and deep feature maps.At the same time,image segmenta-tion and maximum suppression algorithm are used to improve the accuracy of small target traffic sign recognition.The experimental results show that compared with the original YOLOv5s model,the improved model is not only more accurate mAP@0.5,it has in-creased by 8.0%,6.5%and 2.7%respectively,and greatly improved the detection effect of small target traffic signs and traffic signs blocked by objects.The improved YOLOv5s model is superior to the original model and improves the accuracy of traffic sign detec-tion.
Keywords：detection of traffic signsYOLOv5sAttention module(CBAM)BiFPNanchor layer
Publication Date：2025-12-20
Online Publishing Date：2026-03-09(First online date of this platform, not the publication date of the document)
Pages：6( 3513-3518 )