Fast crack detection method for large-size concrete surface images using percolation-based image processing

Tomoyuki Yamaguchi, Shuji Hashimoto

    Research output: Contribution to journalArticle

    217 Citations (Scopus)

    Abstract

    The detection of cracks on concrete surfaces is the most important step during the inspection of concrete structures. Conventional crack detection methods are performed by experienced human inspectors who sketch crack patterns manually; however, such detection methods are expensive and subjective. Therefore, automated crack detection techniques that utilize image processing have been proposed. Although most the image-based approaches focus on the accuracy of crack detection, the computation time is also important for practical applications because the size of digital images has increased up to 10 megapixels. We introduce an efficient and high-speed crack detection method that employs percolation-based image processing. We propose termination-and skip-added procedures to reduce the computation time. The percolation process is terminated by calculating the circularity during the processing. Moreover, percolation processing can be skipped in subsequent pixels according to the circularity of neighboring pixels. The experimental result shows that the proposed approach efficiently reduces the computation cost.

    Original languageEnglish
    Pages (from-to)797-809
    Number of pages13
    JournalMachine Vision and Applications
    Volume21
    Issue number5
    DOIs
    Publication statusPublished - 2010 Aug

    Keywords

    • Concrete surface
    • Crack detection
    • Large-size image
    • Percolation

    ASJC Scopus subject areas

    • Hardware and Architecture
    • Computer Vision and Pattern Recognition
    • Software
    • Computer Science Applications

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