Improved YOLOv5 based on repVGG and ECA prediction head for real-time traffic sign recognition

Traffic sign recognition is an important task in driverless technology. Because the traffic signs are located in a complex environment and the target scale changes greatly, it brings a burden to the network optimization. In reality, it is usually difficult for common models to achieve good detection accuracy while ensuring the detection speed. In order to solve this problem, we introduce the structural re-parameterization technique on the basis of YOLOv5, try to take advantage of the ad-vantages of high performance of multi-branch model and fast reasoning of single-channel model at the same time, and embed the effective channel attachment (ECA) module on the prediction head to find the attention area in complex scenes. Extensive experiments on Tsinghua-Tent 100K (TT100K) dataset show that our improved model can give consideration to recognition speed and accuracy with practical application value