Pavement defect detection is of profound significance regarding road safety, so it has been a trending research topic. In the past years, deep learning based methods have turned into a key technology, with advantages of high accuracy, strong robustness, and adaptability to complex pavement environments. This paper first reviews the methods based on image processing and 3D imaging. As for image-based processing techniques, they can be classified into three types based on how to label the collected data: fully supervised learning, unsupervised learning, and other methods. Different methods are further classified and compared with each other. Second, the pavement detection methods based on 3D data are sorted out, thereby summarizing their benefits, drawbacks, and application scenarios. Third, the study proposed the major challenges in the field of pavement defect detection, introduced validated datasets and evaluation metrics. Finally, on the basis of reviewing the literature in pavement defect detection, the promising direction is put forward.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Pavement Defect Detection With Deep Learning: A Comprehensive Survey


    Beteiligte:
    Fan, Lili (Autor:in) / Wang, Dandan (Autor:in) / Wang, Junhao (Autor:in) / Li, Yunjie (Autor:in) / Cao, Yifeng (Autor:in) / Liu, Yi (Autor:in) / Chen, Xiaoming (Autor:in) / Wang, Yutong (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2024-03-01


    Format / Umfang :

    6421571 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Deep-Learning-Based Intelligent PotholeEye+ Detection Pavement Distress Detection System

    Gorintla, Shobana / Kumar, B. Anil / Chanadana, B. Sai et al. | IEEE | 2022


    Deep Learning for Visual Tracking: A Comprehensive Survey

    Marvasti-Zadeh, Seyed Mojtaba / Cheng, Li / Ghanei-Yakhdan, Hossein et al. | IEEE | 2022


    Detection of Pavement Maintenance Treatments using Deep-Learning Network

    Gao, Lu / Yu, Yao / Hao Ren, Yi et al. | Transportation Research Record | 2021


    Challenges and Feasibility for Comprehensive Automated Survey of Pavement Conditions

    Wang, K. C. P. / ASCE | British Library Conference Proceedings | 2004