Mobile Laser Scanning (MLS) system can provide high-density and accurate 3D point clouds that enable rapid pavement crack detection for road maintenance tasks. Supervised learning-based algorithms have been proved pretty effective for handling such a large amount of inhomogeneous and unstructured point clouds. However, these algorithms often rely on a lot of annotated data, which is labor-intensive and time-consuming. This paper presents a semi-supervised point-level approach to overcome this challenge. We propose a graph-widen module to construct a reasonable graph structure for point clouds, increasing the detection performance of graph convolutional networks (GCN). The constructed graph characterizes the local features from a small amount of annotated data, avoiding information loss and dramatically reduces the dependence on annotated data. The MLS point clouds acquired by a commercial RIEGL VMX-450 system are used in this study. The experimental results demonstrate that our method outperforms the state-of-the-art point-level methods in terms of recall, F1 score, and efficiency while achieving comparable accuracy.


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    Titel :

    GCN-Based Pavement Crack Detection Using Mobile LiDAR Point Clouds


    Beteiligte:
    Feng, Huifang (Autor:in) / Li, Wen (Autor:in) / Luo, Zhipeng (Autor:in) / Chen, Yiping (Autor:in) / Fatholahi, Sarah Narges (Autor:in) / Cheng, Ming (Autor:in) / Wang, Cheng (Autor:in) / Junior, Jose Marcato (Autor:in) / Li, Jonathan (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2022-08-01


    Format / Umfang :

    7862020 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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