Abstract
Traditionally, surface cracks of concrete structures are manually measured and recorded by experienced inspectors. Recently, many crack detection techniques utilizing image processing have been proposed to detect cracks in 2D piecewise constant image. However, crack images in real applications such as building safety evaluation and bridge maintenance are usually 2D multiple-phase piecewise constant images. Furthermore, these methods require the user to choose appropriate parameters, especially the threshold in image segmentation step, for a certain set of images with similar contrast. This paper presents a novel automatic method for the efficient detection of cracks in 2D multiple-phase piecewise constant crack image without predefined thresholds in image binarization step. Firstly, cracks are enhanced utilizing 2 geometric features of crack including tubular and symmetric properties. In the second step, we proposed a crack-feature based non-linear relaxation method to detect crack from the enhanced images. Finally, we proposed a crack probability based binarization method to extract the binary crack map. The robustness of our proposed method are demonstrated in various crack images of building concrete structures.
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Nguyen, H.N., Phan, V.D., Nguyen, V.B. (2018). A Novel Method of Automatic Crack Detection Utilizing Non-linear Relaxation Method and Crack Geometric Features for Safety Evaluation of Concrete Structure. In: Kaenampornpan, M., Malaka, R., Nguyen, D., Schwind, N. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2018. Lecture Notes in Computer Science(), vol 11248. Springer, Cham. https://doi.org/10.1007/978-3-030-03014-8_7
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DOI: https://doi.org/10.1007/978-3-030-03014-8_7
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