A real-time and high-precision method for small traffic-signs recognition

This article proposes an efficient method for small-size traffic-signs recognition, named traffic-Signs recognition small-aware, with the inspiration of the state-of-the-art object detection framework YOLOv4 and Y OLOv5, and proposes a data augmentation method named Random Erasing-Attention, which can increase difficult samples and enhance the robustness of the model.