19 June 2017 Pattern recognition of concrete surface cracks and defects using integrated image processing algorithms
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Proceedings Volume 10443, Second International Workshop on Pattern Recognition; 1044309 (2017) https://doi.org/10.1117/12.2280933
Event: Second International Workshop on Pattern Recognition, 2017, Singapore, Singapore
Abstract
Pattern recognition of concrete surface crack defects is very important in determining stability of structure like building, roads or bridges. Surface crack is one of the subjects in inspection, diagnosis, and maintenance as well as life prediction for the safety of the structures. Traditionally determining defects and cracks on concrete surfaces are done manually by inspection. Moreover, any internal defects on the concrete would require destructive testing for detection. The researchers created an automated surface crack detection for concrete using image processing techniques including Hough transform, LoG weighted, Dilation, Grayscale, Canny Edge Detection and Haar Wavelet Transform. An automatic surface crack detection robot is designed to capture the concrete surface by sectoring method. Surface crack classification was done with the use of Haar trained cascade object detector that uses both positive samples and negative samples which proved that it is possible to effectively identify the surface crack defects.
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Jessie R. Balbin, Jessie R. Balbin, Carlos C. Hortinela, Carlos C. Hortinela, Ramon G. Garcia, Ramon G. Garcia, Sunnycille Baylon, Sunnycille Baylon, Alexander Joshua Ignacio, Alexander Joshua Ignacio, Marco Antonio Rivera, Marco Antonio Rivera, Jaimie Sebastian, Jaimie Sebastian, } "Pattern recognition of concrete surface cracks and defects using integrated image processing algorithms ", Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 1044309 (19 June 2017); doi: 10.1117/12.2280933; https://doi.org/10.1117/12.2280933
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