Aiming at the low detection accuracy of traditional traffic sign recognition algorithms,a traffic sign recognition method with improved YOLOv5 algorithm is proposed. First,improve the loss function of the YOLOv5 algorithm,use the EIOU loss function instead of the GIOU loss function used by the YOLOv5 algorithm to optimize the training model,improve the accuracy of the algorithm, and achieve faster identification of the target,then use the weighted Cluster NMS to improve the YOLOv5 itself. The weighted NMS algorithm improves the accuracy of generating the detection frame. The experimental results show that the mAP value of the model trained on the CCTSDB traffic sign dataset produced by Changsha University of Science and Technology by the improved YOLOv5 algorithm reaches 84. 35%, which is 6. 23% higher than the original YOLOv5 algorithm. Therefore,the improved YOLOv5 algorithm has higher accuracy in traffic sign recognition and can be better applied to practice.