Research on traffic sign recognition based on multi-scale and coordinate attention
Aiming at the requirement of high detection speed and recognition accuracy for intelligent traffic recognition system,an improved lightweight YOLOv4-3RSCtiny algorithm based on the fusion of multi-scale and coordinate attention mechanism is proposed on the basis of YOLOv4-tiny algorithm.Firstly,the Resblock_body module in the backbone network is improved into a ResblockD lightweight module with fewer parameters as a way to improve the detection speed of the algorithm.Secondly,the feature pyramid pooling network is introduced to enrich the spatial information of the deep feature maps.Next,the coordinate attention mechanism is introduced in the prediction stage to reduce the interference of the background information.Finally,the path enhancement algorithm is proposed by using the path enhancement algorithm with the path-enhanced feature pyramid network with multiple cross-level fusion to improve the recognition rate of the algorithm for small target objects.Finally,by testing the algorithm on the TT100K dataset,the experimental results show that the YOLOv4-3RSCtiny algorithm has a better performance compared to YOLOv4-tiny,with higher accuracy and better real-time performance.
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