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Proceedings Paper

Vision-based concrete crack detection technique using cascade features
Author(s): Rahmat Ali; Dharshan Lokekere Gopal; Young-Jin Cha
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Paper Abstract

This paper presents an existing face detection method using cascade features updated for determining the cracks on concrete surfaces. The main goal of structural health monitoring (SHM) is to safeguard our existing structures from cracks, corrosion, delamination, and spalls due to incessant use of structures. Cracks are the foremost defect that will occur in the structures, and they require quick attention before they lead to structural failure; it is a laborious job to detect the cracks using personnel (visual inspection) practices, which produce highly unreliable results. The results of contact sensor-based crack detection techniques, however, mainly depend on parameters such as temperature, sensitivity, accessibility, etc. Recently there has been high expansion in computer vision (image processing) techniques that facilitate the detection of cracks. In this study, a modified cascade face detection technique based on the Viola-Jones algorithm is proposed to detect cracks in concrete walls. Cascade features calculated from the Viola-Jones algorithm are trained on positive and negative datasets of images with and without cracks. Once training is completed, the Viola-Jones algorithm spots the cracks on test images with bounding boxes drawn around the region of the cracks.

Paper Details

Date Published: 27 March 2018
PDF: 7 pages
Proc. SPIE 10598, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, 105980L (27 March 2018); doi: 10.1117/12.2295962
Show Author Affiliations
Rahmat Ali, Univ. of Manitoba (Canada)
Dharshan Lokekere Gopal, Univ. of Manitoba (Canada)
Young-Jin Cha, Univ. of Manitoba (Canada)

Published in SPIE Proceedings Vol. 10598:
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018
Hoon Sohn, Editor(s)

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