Traffic sign detection algorithm based on improved Faster R-CNN

Aiming at the problems of complex background and small traffic sign target in large view traffic scene, an improved Faster R-CNN detection network algorithm is proposed. Firstly, the deep residual network ResNet50 is used as the backbone network to extract the features of traffic signs. Secondly, the strategy of using reasonable scale sliding window on two different level feature maps is designed to generate the target proposal region to enhance the detection ability of multi-scale traffic signs. Finally, the attention mechanism module is introduced into the residual block to strengthen the key information of the image and suppress the image background information. The validity of the algorithm is verified on the Chinese traffic sign dataset, with an average detection accuracy of 98.52% and a detection rate of 0.042 s per image. The detection effect of the improved algorithm is obviously better than the original Faster R-CNN detection method, and is more suitable for traffic sign detection in complex scenes, with strong robustness.