The use of helmets plays a crucial role in mitigating head injuries resulting from traffic accidents. However, manual supervision of helmet usage is both costly and prone to missing target objects. With the advancement of technology and widespread use of surveillance cameras, it has become technically feasible to automatically detect riders’ helmet wearing based on roadside videos. This capability holds significant potential in effectively guiding riders to wear helmets and enhancing driving safety. This paper presents a multi-network-based approach for automated detection of helmet wearing among electric bicycle riders. The object detection model is employed to detect electric bicycles and helmets. Additionally, the overall approach utilizes a human pose estimator to detect keypoints of human skeletons for selecting the region of interest (RoI) corresponding to the head region location. We propose location rules to extract the rider region within the electric bicycle box for distinguishing riders and locate the head region based on human skeleton keypoints for helmet wearing matching. The mean average precision (mAP) of the detection model trained on public datasets reaches 91.8%. To evaluate matching accuracy, we randomly selected 150 correctly detected images from the open-sourced two-wheeler helmet dataset (TWHD), achieving a matching accuracy of 95.6%. The detection model is trained and tested on public datasets which effectively facilitates the processes of data collection and annotation. Based on multi-network information fusion, we can accurately detect helmet violations among electric bicycle riders in multi-person interaction scenarios.


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    Title :

    Detection of Helmet Violations among Electric Bicycle Riders Through Multi-Network


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:
    Huang, Shize (author) / Zhang, Yi (author) / Li, Xingying (author) / Qin, Jinzhe (author) / Fan, Qianhui (author) / Xia, Guoqiang (author)


    Publication date :

    2024-12-12




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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