Computer vision-based method for concrete crack detection

Dinh, TH; Ha, QP; La, HM

Computer vision-based method for concrete crack detection

Dinh, TH Ha, QP

La, HM

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Publication Type:: Conference Proceeding
Citation:: 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016, 2017
Issue Date:: 2017-01-31

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Field	Value	Language
dc.contributor.author	Dinh, TH	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.contributor.author	La, HM	en_US
dc.date.issued	2017-01-31	en_US
dc.identifier.citation	2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016, 2017	en_US
dc.identifier.isbn	9781509035496	en_US
dc.identifier.uri	http://hdl.handle.net/10453/89624
dc.description.abstract	© 2016 IEEE. This paper presents a computer vision-based method to automatically detect concrete cracks. We focus on images containing the concrete: background and crack, where the background is the major mode of the gray-scale histogram. Therefore, we address the detection problem of potential concrete cracks by dealing with histogram thresholding to extract regions of interests from the background. We first employ line emphasis and moving average filters to remove noise from concrete surface images obtained from an inspection robot. The developed algorithm is then applied for automatic detection of significant peaks from the gray-scale histogram of the smoothed image. The biggest peak and its corresponding valley(s) are consequently identified to calculate the threshold value for image binarization. The effectiveness of our proposed method was successfully evaluated on various test images, where cracks could be identified without the requirement of some heuristic reasoning.	en_US
dc.relation.ispartof	2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016	en_US
dc.relation.isbasedon	10.1109/ICARCV.2016.7838682	en_US
dc.title	Computer vision-based method for concrete crack detection	en_US
dc.type	Conference Proceeding
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. This paper presents a computer vision-based method to automatically detect concrete cracks. We focus on images containing the concrete: background and crack, where the background is the major mode of the gray-scale histogram. Therefore, we address the detection problem of potential concrete cracks by dealing with histogram thresholding to extract regions of interests from the background. We first employ line emphasis and moving average filters to remove noise from concrete surface images obtained from an inspection robot. The developed algorithm is then applied for automatic detection of significant peaks from the gray-scale histogram of the smoothed image. The biggest peak and its corresponding valley(s) are consequently identified to calculate the threshold value for image binarization. The effectiveness of our proposed method was successfully evaluated on various test images, where cracks could be identified without the requirement of some heuristic reasoning.

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http://hdl.handle.net/10453/89624