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<p>This paper proposes a lightweight traffic sign detection system based on you only look once (YOLO). Firstly, the classification to fusion (C2f) structure is integrated into the backbone network, employing deformable convolution and bi-directional feature pyramid network (BiFPN)_Concat to improve the adaptability of the network. Secondly, the simple attention module (SimAm) is embedded to prioritize key features and reduce the complexity of the model after the C2f layer at the end of the backbone network. Next, the focal efficient intersection over union (EIoU) is introduced to adjust the weights of challenging samples. Finally, we accomplish the design and deployment for the mobile app. The results demonstrate improvements, with the <i>F</i>1 score of 0.898 7, mean average precision (<i>mAP</i>)@0.5 of 98.8%, <i>mAP</i>@0.5: 0.95 of 75.6%, and the detection speed of 50 frames per second (FPS).</p>

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YOLO-based lightweight traffic sign detection algorithm and mobile deployment

  • 期刊论文

  • 发表日期：

    2025年2月14日

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  • Yaqin Wu,
  • Tao Zhang,
  • Jianjun Niu,
  • Yan Chang,
  • Ganjun Liu

摘要

This paper proposes a lightweight traffic sign detection system based on you only look once (YOLO). Firstly, the classification to fusion (C2f) structure is integrated into the backbone network, employing deformable convolution and bi-directional feature pyramid network (BiFPN)_Concat to improve the adaptability of the network. Secondly, the simple attention module (SimAm) is embedded to prioritize key features and reduce the complexity of the model after the C2f layer at the end of the backbone network. Next, the focal efficient intersection over union (EIoU) is introduced to adjust the weights of challenging samples. Finally, we accomplish the design and deployment for the mobile app. The results demonstrate improvements, with the F1 score of 0.898 7, mean average precision (mAP)@0.5 of 98.8%, mAP@0.5: 0.95 of 75.6%, and the detection speed of 50 frames per second (FPS).