Improved Faster R-CNN Traffic Sign Detection Algorithm Based on Transformer

Abstract

In view of the fact that traffic sign detection is becoming more and more important in assisted driving, but there are situations where the target is small, occluded and the background is complex, this paper proposes a Transformer-based improved Faster R-CNN algorithm for traffic sign detection. The algorithm in this paper uses the Transformer network based on the shift window as the backbone. The multi-level feature map is fused across layers through the cascade fusion module to obtain the fusion feature map. Moreover, the model’s feature extraction ability is improved. The detection head module eliminates rounding through RoI Align quantization error. Based on experiments in the TT100K dataset, it can be known that the mAP and detection speed of the algorithm in this paper has been improved. The effectiveness of the improved model in this paper is proven and it is applicable to real scenarios.