Cracks are important indexes to evaluate the health status of concrete structures. To accurately and automatically identify the cracks of concrete structures, and solve the time-consuming and labor-intensive limits of manual detection methods, this paper proposed an image-based concrete cracks identification method based on a lightweight Convolutional Neural Network, which includes three modules: crack classification, semantic segmentation and quantitative calculation of crack geometric size. Firstly, the S_MobileNet was used to classify cracks, exclude irrelevant regions, and reduce the interference of non-crack images; Secondly, the optimized method SM-UNet based on the U-Net network was employed to segment the detected crack image at the pixel level; Finally, based on the results of crack semantic segmentation, image post-processing technology was used to realize the quantitative calculation of crack geometric size parameters, which provides a basis for crack damage assessment of concrete structures. The experimental results show that this study provides a solution for the automatic detection of crack images and high-precision measurement of crack size, which has an important value in scientific research and engineering application.


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    Titel :

    Image-based Concrete Cracks Identification under Complex Background with Lightweight Convolutional Neural Network


    Weitere Titelangaben:

    KSCE J Civ Eng


    Beteiligte:
    Meng, Qingcheng (Autor:in) / Hu, Lei (Autor:in) / Wan, Da (Autor:in) / Li, Mingjian (Autor:in) / Wu, Haojie (Autor:in) / Qi, Xin (Autor:in) / Tian, Yongding (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.12.2023


    Format / Umfang :

    12 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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