MTSDet: multi-scale traffic sign detection with attention and path aggregation

To solve the problem that existing traffic signs are not easily detected leading to low detection performance due to their small sizes and external factors such as weather conditions, this paper proposes a traffic sign detection method, MTSDet (Multi-scale Traffic Sign Detection with attention and path aggregation), which focuses on the multi-scale detection problem and effectively improves the detection performance. First, the method efficiently extracts semantic features by introducing the Attention Mechanism Network(AMNet), and then feeds the multi-scale semantic features into Path Aggregation Feature Pyramid Network(PAFPN) for multi-scale feature fusion to obtain multi-scale advanced semantic features. Finally, the multi-scale advanced semantic feature map is deformable interest pooled to effectively enhance the multi-scale object detection modeling capability. In this paper, the above method is validated by two classical datasets, German traffic sign detection dataset and Chinese traffic sign detection dataset, which achieve 92.9% and 94.3% mAP, respectively, and have obvious detection accuracy improvement when compared with other classical advanced algorithms, effectively proving the superiority and generalization of the algorithm in this paper. Code is available at haps://github.com/why529913/MTSDet