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Pattern recognition of concrete surface cracks and defects using integrated image processing algorithms
Author(s): Jessie R. Balbin; Carlos C. Hortinela; Ramon G. Garcia; Sunnycille Baylon; Alexander Joshua Ignacio; Marco Antonio Rivera; Jaimie Sebastian
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Paper 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.

Paper Details

Date Published: 19 June 2017
PDF: 5 pages
Proc. SPIE 10443, Second International Workshop on Pattern Recognition, 1044309 (19 June 2017); doi: 10.1117/12.2280933
Show Author Affiliations
Jessie R. Balbin, Mapúa Institute of Technology (Philippines)
Carlos C. Hortinela, Mapúa Institute of Technology (Philippines)
Ramon G. Garcia, Mapúa Institute of Technology (Philippines)
Sunnycille Baylon, Mapúa Institute of Technology (Philippines)
Alexander Joshua Ignacio, Mapúa Institute of Technology (Philippines)
Marco Antonio Rivera, Mapúa Institute of Technology (Philippines)
Jaimie Sebastian, Mapúa Institute of Technology (Philippines)


Published in SPIE Proceedings Vol. 10443:
Second International Workshop on Pattern Recognition
Xudong Jiang; Masayuki Arai; Guojian Chen, Editor(s)

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