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Abstract <p>To improve the accuracy of traffic sign recognition in complex backgrounds and extreme conditions, an improved YOLO network deep learning method is proposed. This method achieves cross scale connection and fast normalization fusion of multiple features through label smoothing and loss function improvement, and introduces a mixed attention mechanism to enhance the robustness of the recognition process. The experimental results show that our method can effectively cope with the impact of complex backgrounds and extreme conditions on the recognition process, and the accuracy of traffic sign recognition is significantly higher than the three methods of CNN, RNN, and YOLO.</p>

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Research on Traffic Sign Image Recognition Algorithm Based on Improved Yolo Deep Network

  • 期刊论文

  • 发表日期：

    2025年10月1日

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  • Shuang Liu,
  • Jie Lei,
  • Dequan Zheng

摘要

Abstract

To improve the accuracy of traffic sign recognition in complex backgrounds and extreme conditions, an improved YOLO network deep learning method is proposed. This method achieves cross scale connection and fast normalization fusion of multiple features through label smoothing and loss function improvement, and introduces a mixed attention mechanism to enhance the robustness of the recognition process. The experimental results show that our method can effectively cope with the impact of complex backgrounds and extreme conditions on the recognition process, and the accuracy of traffic sign recognition is significantly higher than the three methods of CNN, RNN, and YOLO.