A traffic sign recognition algorithm based on feature concentration pyramid was proposed to solve the problems of distorted target and small target detection.Firstly,the original feature fusion network is improved by using the feature concentration pyramid,and the global remote dependence is captured by a lightweight multi-layer percep-tron(MLP).The local corner region of the input image is captured by the parallel learning vision center mechanism(LVC),which improves the detection accuracy of distorted targets and small targets.Secondly,recursive gated convo-lution is utilized to extract high-order spatial interaction information of shallow feature maps,which is beneficial to improve the detection effect of small targets.Finally,the SIoU regression loss function is used,with angle loss intro-duced to redefine the penalty index.It not only reduces the degree of freedom of total loss,preventing the prediction box from wandering around during training,but also speeds up the convergence rate to make the positioning more ac-curate.Numerous experiments on the TT100K data set demonstrate that the average detection accuracy is 93.4%,which is 3.5% higher than that of the traditional YOLOv5n,and the frame processing speed reaches up to 94.34fps.
维普
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