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Traffic-Sign-Detection Algorithm Based on SK-EVC-YOLO

Author

Listed:
  • Faguo Zhou

    (School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing 100083, China)

  • Huichang Zu

    (School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing 100083, China)

  • Yang Li

    (School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing 100083, China)

  • Yanan Song

    (School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing 100083, China)

  • Junbin Liao

    (School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing 100083, China)

  • Changshuo Zheng

    (School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing 100083, China)

Abstract

Traffic sign detection is an important research direction in the process of intelligent transportation in the Internet era, and plays a crucial role in ensuring traffic safety. The purpose of this research is to propose a traffic-sign-detection algorithm based on the selective kernel attention (SK attention), explicit visual center (EVC), and YOLOv5 model to address the problems of small targets, incomplete detection, and insufficient detection accuracy in natural and complex road situations. First, the feature map with a smaller receptive field in the backbone network is fused with other scale feature maps to increase the small target detection layer. Then, the SK attention mechanism is introduced to extract and weigh features at different scales and levels, enhancing the attention to the target. By fusing the explicit visual center to gather local area features within the layer, the detection effect of small targets is improved. According to the experiment results, the mean average precision (mAP) on the Tsinghua-Tencent Traffic Sign Dataset (TT100K) for the proposed algorithm is 88.5%, which is 4.6% higher than the original model, demonstrating the practicality of the detection of small traffic signs.

Suggested Citation

  • Faguo Zhou & Huichang Zu & Yang Li & Yanan Song & Junbin Liao & Changshuo Zheng, 2023. "Traffic-Sign-Detection Algorithm Based on SK-EVC-YOLO," Mathematics, MDPI, vol. 11(18), pages 1-12, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3873-:d:1237542
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    References listed on IDEAS

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    1. Hui Wang & Xun Zhang & Shengchuan Jiang, 2022. "A Laboratory and Field Universal Estimation Method for Tire–Pavement Interaction Noise (TPIN) Based on 3D Image Technology," Sustainability, MDPI, vol. 14(19), pages 1-21, September.
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