Improved YOLOv5 Traffic Sign Detection Algorithm

Traffic sign detection has been widely used in intelligent transportation systems such as automatic driving and assisted driving, and its detection performance is related to driving safety. Aiming at the problem that the existing object detection algorithm has poor detection effect on traffic signs with small size, low resolution and non-obvious features in the image, a traffic sign detection algorithm based on improved YOLOv5s is proposed. The 80×80 small sensing field object detection layer in the original algorithm is changed to a smaller 160×160 detection layer, which improves the detection ability of the network model for small object of traffic signs and reduces the missed detection rate of small object. The attention context module(ACM)is constructed to obtain the characteristic information of the target and its adjacent regions from different receptive fields for each branch, and the attention mechanism is used to make the network pay more attention to the traffic signs in the image and avoid being affected by other complex information. The feature fusion module (FFM)is added to filter out the useless information on different layers and retain only the useful information for the model to detect traffic signs. The tacit knowledge is added to refine the output of the detection layer. Experimental results show that the improved algorithm has a recall rate and average accuracy of 95.2% and 97.2% on the CCTSDB traffic sign detection dataset, which is improved compared with the original model, and the effect is significantly improved under medium and long-distance small object detection, and the simultaneous detection speed is 47.3 FPS to meet the real-time requirements. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.