Crack detection using enhanced thresholding on UAV based collected images

Zhu, Q; Dinh, TH; Hoang, VT; Phung, MD; Ha, QP

Crack detection using enhanced thresholding on UAV based collected images

Zhu, Q Dinh, TH Hoang, VT Phung, MD

Ha, QP

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Publication Type:: Conference Proceeding
Citation:: Australasian Conference on Robotics and Automation, ACRA, 2018, 2018-December
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Zhu, Q	en_US
dc.contributor.author	Dinh, TH	en_US
dc.contributor.author	Hoang, VT	en_US
dc.contributor.author	Phung, MD https://orcid.org/0000-0002-8870-5114	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.date.accessioned	2020-03-31T05:06:36Z
dc.date.available	2020-03-31T05:06:36Z
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Australasian Conference on Robotics and Automation, ACRA, 2018, 2018-December	en_US
dc.identifier.issn	1448-2053	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139648
dc.description.abstract	© 2018 Australasian Robotics and Automation Association. All rights reserved. This paper proposes a thresholding approach for crack detection in an unmanned aerial vehicle (UAV) based infrastructure inspection system. The proposed algorithm performs recursively on the intensity histogram of UAV-taken images to exploit their crack-pixels appearing at the low intensity interval. A quantified criterion of interclass contrast is proposed and employed as an object cost and stop condition for the recursive process. Experiments on different datasets show that our algorithm outperforms different segmentation approaches to accurately extract crack features of some commercial buildings.	en_US
dc.relation.ispartof	Australasian Conference on Robotics and Automation, ACRA	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Crack detection using enhanced thresholding on UAV based collected images	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-December	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
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2018-December	en_US

Abstract:

© 2018 Australasian Robotics and Automation Association. All rights reserved. This paper proposes a thresholding approach for crack detection in an unmanned aerial vehicle (UAV) based infrastructure inspection system. The proposed algorithm performs recursively on the intensity histogram of UAV-taken images to exploit their crack-pixels appearing at the low intensity interval. A quantified criterion of interclass contrast is proposed and employed as an object cost and stop condition for the recursive process. Experiments on different datasets show that our algorithm outperforms different segmentation approaches to accurately extract crack features of some commercial buildings.

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http://hdl.handle.net/10453/139648