Abstract: Traffic sign images captured in  complex weather conditions suffer from reduced clarity and increased recognition difficulty, making it challenging for existing algorithms to accurately identify them. To address this issue, an improved traffic sign recognition algorithm based on YOLOv8 is proposed. Firstly, according to the idea of residual learning, a feature map enhancement module is designed to replace the residual block of C2f in the backbone network to improve the feature extraction ability of the backbone network. Secondly, on the basis of coordinate attention (CA), features are grouped and 3×3 convolution branches are added to realize cross-spatial information aggregation, which realizes the capture of finer features and makes the model focus more on the target area rather than the background. Then, the hybrid pooling is used to optimize the spatial pyramid pooling network to improve the feature expression ability of the model. Finally, in order to enhance the expression ability of the target multi-scale features, a multi-scale feature fusion network based on feature recombination and double-branch downsampling is designed to effectively promote the information interaction between different levels of features. Experiments were carried out on the self-made complex weather traffic sign dataset SWTSD. The mean average precision reaches 90.4%, outperforming the baseline algorithm by 3.9%, and the FPS reaches 109.4, which can meet the real-time requirements.

Key words: YOLOv8 algorithm, traffic sign recognition, residual network, hybrid pooling, multi-scale feature