2013
DOI: 10.7763/ijet.2014.v5.596
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Application of Computer Vision to Crack Detection of Concrete Structure

Abstract: Abstract-Most important civil infrastructures are made of concrete, so accurate information by routine inspection is necessary for structure maintenance. Sometimes temporarily erected scaffoldings are needed for infrastructure inspections. Bridge inspection for example, the inspectors must stand on the platform to examine the underside of a bridge, but such a procedure is risky. At present, several inspection systems coupled with Charge Coupled Devices (CCD) cameras have been developed and applied to infrastru… Show more

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Cited by 22 publications
(12 citation statements)
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“…Some studies presented their methodologies based on machine learning and neural networks for identification and quantification of cracks starting from images collected by remote-controlled robots (Jahanshahi et al, 2013;Prasanna et al, 2016). Other similar studies based on images for the analysis of cracks have been presented (Lins and Givigi, 2016;Nguyen et al, 2014;Su, 2013;Valença et al, 2013). Usually computer vision functions are used to automatically extract the geometric properties of the damaged elements.…”
Section: Literary Reviewmentioning
confidence: 99%
“…Some studies presented their methodologies based on machine learning and neural networks for identification and quantification of cracks starting from images collected by remote-controlled robots (Jahanshahi et al, 2013;Prasanna et al, 2016). Other similar studies based on images for the analysis of cracks have been presented (Lins and Givigi, 2016;Nguyen et al, 2014;Su, 2013;Valença et al, 2013). Usually computer vision functions are used to automatically extract the geometric properties of the damaged elements.…”
Section: Literary Reviewmentioning
confidence: 99%
“…In related studies on image processing techniques such as ones that applied the Otsu method [9], which is based on shooting variables and threshold value settings, to concrete structures such as roads, tunnels, and bridges to detect and measure cracks [10][11][12][13], image preprocessing studies have been conducted using histograms, mean values, median values, and Gaussian filters. Studies on feature extraction using morphology techniques [14][15][16] and studies on contour detection through primary and secondary differentiation [17,18] have also been conducted and repeatedly developed. Thereafter, the field of applicable target structures in studies conducted on crack detection was expanded to include bridges, tunnels, and dams that cannot be easily accessed.…”
Section: Introductionmentioning
confidence: 99%
“…Xu et al [70] resized the resolution of rectangular raw images of damaged reinforced concrete columns into smaller square pixels image to reduce calculation costs on the supervised learning procedure. Su [71] downsized 100 sampling images that were used in training model for concrete pavement to reduce computational time. Xu et al [72] resized the height pixel unit of grayscale images using bicubic interpolation then cropped them into smaller elements as the input of the deep network.…”
Section: Introductionmentioning
confidence: 99%