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Research on Urban Traffic Incident Detection Based on Vehicle Cameras

Author

Listed:
  • Zhuofei Xia

    (Institute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, China
    Shiyan Industry Technique Academy of Chinese Academy of Engineering, Shiyan 442002, China)

  • Jiayuan Gong

    (Institute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, China
    Shiyan Industry Technique Academy of Chinese Academy of Engineering, Shiyan 442002, China)

  • Hailong Yu

    (Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Wenbo Ren

    (Institute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, China
    Shiyan Industry Technique Academy of Chinese Academy of Engineering, Shiyan 442002, China)

  • Jingnan Wang

    (Institute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan 442002, China
    Shiyan Industry Technique Academy of Chinese Academy of Engineering, Shiyan 442002, China)

Abstract

Situational detection in the traffic system is of great significance to traffic management and even urban management. Traditional detection methods are generally based on roadside equipment monitoring roads, and it is difficult to support large-scale and fine-grained traffic incident detection. In this study, we propose a detection method applied to the mobile edge, which detects traffic incidents based on the video captured by vehicle cameras, so as to overcome the limitations of roadside terminal perception. For swarm intelligence detection, we propose an improved YOLOv5s object detection network, adding an atrous pyramid pooling layer to the network and introducing a fusion attention mechanism to improve the model accuracy. Compared with the raw YOLOv5s, the mAP metrics of our improved model are increased by 3.3% to 84.2%, enabling it to detect vehicles, pedestrians, traffic accidents, and fire traffic incidents on the road with high precision in real time. This provides information for city managers to help them grasp the abnormal operation status of roads and cities in a timely and effective manner.

Suggested Citation

  • Zhuofei Xia & Jiayuan Gong & Hailong Yu & Wenbo Ren & Jingnan Wang, 2022. "Research on Urban Traffic Incident Detection Based on Vehicle Cameras," Future Internet, MDPI, vol. 14(8), pages 1-17, July.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:8:p:227-:d:872591
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    References listed on IDEAS

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    1. Shang, Pengjian & Li, Xuewei & Kamae, Santi, 2005. "Chaotic analysis of traffic time series," Chaos, Solitons & Fractals, Elsevier, vol. 25(1), pages 121-128.
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