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<p>Road traffic sign recognition is an essential function of modern autonomous driving systems. Accurate recognition plays an important role in ensuring vehicle safety and enabling subsequent operations. This paper introduces a Siamese architecture that is combined with feature reconstruction to overcome current method limitations. In detail, the proposed method enhances recognition accuracy by reconstructing convolutional features within a Siamese Neural Network (SNN) framework, integrating an improved Mamba network with mainstream CNN architectures, including VGG-16, AlexNet, ResNet, or MobileNetV2. Through comprehensive architectural comparisons, the optimal network configuration is determined for different application scenarios. Utilizing this approach, the accuracy of traffic sign recognition is substantially improved, addressing the shortcomings of existing technologies. Extensive experimental validation was conducted on multiple datasets. Taking VGG-16 network as an example, the experimental results demonstrated the method’s effectiveness, achieving accuracies of 99.83% on the GTSRB dataset, 99.13% on the TSRD dataset, and 99.07% on the TT100K dataset.</p>

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Improved road traffic sign recognition from feature reconstruction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月28日

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  • Gang Huang,
  • Huiling Cao,
  • Jiayue Sun,
  • Zhenling Chen,
  • Zhe Zhou

摘要

Road traffic sign recognition is an essential function of modern autonomous driving systems. Accurate recognition plays an important role in ensuring vehicle safety and enabling subsequent operations. This paper introduces a Siamese architecture that is combined with feature reconstruction to overcome current method limitations. In detail, the proposed method enhances recognition accuracy by reconstructing convolutional features within a Siamese Neural Network (SNN) framework, integrating an improved Mamba network with mainstream CNN architectures, including VGG-16, AlexNet, ResNet, or MobileNetV2. Through comprehensive architectural comparisons, the optimal network configuration is determined for different application scenarios. Utilizing this approach, the accuracy of traffic sign recognition is substantially improved, addressing the shortcomings of existing technologies. Extensive experimental validation was conducted on multiple datasets. Taking VGG-16 network as an example, the experimental results demonstrated the method’s effectiveness, achieving accuracies of 99.83% on the GTSRB dataset, 99.13% on the TSRD dataset, and 99.07% on the TT100K dataset.