Unmanned Aerial Vehicle (UAV)-based pavement distress detection offers efficient and safe advantages. However, obstructions from road vehicles and the slender shape of cracks in UAV images challenge accuracy. To address this, this study established specific flight parameters, proposed the Historical Best Matching Image (HBMI) approach for data loss due to obstructions, and created the UAV-Crack500 dataset with 500 finely annotated crack images. Three algorithms with different loss functions were investigated, finding that the U-Net network combined with our Completely Asymmetric Loss (CAL) achieved the best performance, resolving the issue of class imbalance. Morphological analysis of the semantically segmented images provided precise crack morphology features. In complex scenarios, errors in features like crack area, length, mean width, and maximum width remained within 16%. This study establishes a comprehensive UAV-based pavement distress detection system, overcoming obstructions for accurate assessment.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Unmanned Aerial Vehicle (UAV)-Based Pavement Image Stitching Without Occlusion, Crack Semantic Segmentation, and Quantification


    Contributors:
    Shan, Jinhuan (author) / Jiang, Wei (author) / Huang, Yue (author) / Yuan, Dongdong (author) / Liu, Yaohan (author)

    Published in:

    Publication date :

    2024-11-01


    Size :

    3795169 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Image Segmentation-Based Unmanned Aerial Vehicle Safe Navigation

    Agrawal, Pooja / Ratnoo, Ashwini / Ghose, Debasish | AIAA | 2017


    Unmanned aerial vehicle pavement marking machine

    PAN YUE / LAN CHENGYAN | European Patent Office | 2024

    Free access

    An improved ORB algorithm for the Unmanned Aerial Vehicle (UAV) image stitching task

    Yan, Qicheng / Qiu, Hao | British Library Conference Proceedings | 2022


    Crack detection unmanned aerial vehicle based on visual image

    KIM TAE-MIN / WANG ZHIYUAN | European Patent Office | 2024

    Free access