Application of improved YOLOv5s algorithm in traffic sign detection and recognition

Aiming at the problem of low detection and recognition accuracy of traffic signs in complex traffic scenes, a target detection and recognition method based on improved YOLOv5s algorithm was proposed. Iterative self-organizing data analysis techniques algorithm was used for clustering analysis of TT100K data set to select the prior frame which was more suitable for the size of traffic signs. The new prior frame could cover the size of traffic signs more comprehensively and improve the detection accuracy of the model. The feature map was upsampled to obtain a larger scale feature map, and then contacted with the feature map of the backbone network to obtain a new feature map with more abundant feature information. The new feature map was used for small target detection and recognition, which improved the accuracy of small target detection and recognition. And the difference of the width ratio and height ratio between the real frame and the prior frame was used to replace the difference of the aspect ratio between the real frame and the prior frame to improve the positioning loss function, which solved the problem of penalty disappearing when the width ratio was the same but the actual size was different. Experimental results show that compared with the original YOLOv5s algorithm, the improved algorithm can improve the mean average precision by 9.55%, and has better performance in detecting and recognizing small targets.