Traffic sign detection algorithm based on improved YOLOv5

Aiming at the problems of low detection accuracy and large weight files in the traditional traffic sign recognition algorithm, it is not suitable for practical application. A traffic sign recognition method based on the improved YOLOv5 algorithm is proposed. First, improve the loss function of YOLOv5, use the DIOU loss function to optimize the training model, improve the accuracy of the algorithm, and achieve faster target recognition. Combined with the lightweight convolutional neural network MobileNetv2, the lightweight improvement of the YOLOv5 network is achieved.