Road safety has always been one of the main concerns. With the development of deep learning, computer vision has begun to be used in road damage detection. It has the advantages of faster detection speed, lower cost, easier deployment, etc., which greatly reduces traffic accidents caused by the road. We design an automatic detection system for road damages and deploy it on NVIDIA Jetson Xavier NX. A dataset named FocusCrack is collected under diversiform roads and various lighting conditions. It contains six types of diseases, a total of 4181 images and 5812 labels. Compared with performance of several mainstream algorithms like Faster R-CNN and Single Shot MultiBox Detector (SSD), the model adopts the You Only Look Once v5s(YOLOv5s) algorithm. After experimental testing, the precision, recall, mAP@0.5, and mAP@0.5:0.95 are 90.1%, 91.3%, 93.8%, and 51.9%. The system has achieved good results in practical application.


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    Titel :

    An Automatic Pavement Crack Detection System with FocusCrack Dataset


    Beteiligte:
    Yan, Xinyun (Autor:in) / Shi, Shang (Autor:in) / Xu, Xiaohu (Autor:in) / He, Zhengran (Autor:in) / Zhou, Xiaofeng (Autor:in) / Wang, Chishe (Autor:in) / Lu, Zhiyi (Autor:in)


    Erscheinungsdatum :

    01.09.2022


    Format / Umfang :

    3006760 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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