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dc.contributor.authorNayyeri, F
dc.contributor.authorHou, L
dc.contributor.authorZhou, J
dc.contributor.authorGuan, H
dc.contributor.authorLiew, Alan
dc.date.accessioned2020-02-03T01:47:03Z
dc.date.available2020-02-03T01:47:03Z
dc.date.issued2017
dc.identifier.isbn9781925553055
dc.identifier.urihttp://hdl.handle.net/10072/391061
dc.description.abstractA reliable crack detection system is essential for automatic safety inspection of infrastructures such as roads and bridges. Queensland's population is increasing, putting greater pressure on our already aging civil infrastructure. To get the most out of government investment in our State's thousands of bridges, we need to find quicker, cheaper and more reliable ways to assess and maintain them. In this paper, we propose a novel method for crack detection via salient structure extraction from textured background. This method contains two key steps. In the first step, we extract strong edges and distinguish them from strong textures in a local neighborhood via a relative total variation approach. In the second step, the spatial distribution of texture features are calculated so as to detect cracks as salient structures that are not widely spread across the whole image. The outputs from these two steps are fused to calculate the final structure saliency map which is then binarised to generate the crack masks. This method was evaluated on a crack dataset with images collected from the Internet. Comparison with several alternative approaches shows the superior performance of our method.
dc.description.peerreviewedYes
dc.publisherSHMII
dc.publisher.urihttps://ishmii.org/events-meetings/events-meetings/shmii-8-brisbane/
dc.relation.ispartofconferencename8 th International Conference on Structural Health Monitoring of Intelligent Infrastructure
dc.relation.ispartofconferencetitleSHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Proceedings
dc.relation.ispartofdatefrom2017-12-05
dc.relation.ispartofdateto2017-12-08
dc.relation.ispartoflocationBrisbane, Australia
dc.relation.ispartofpagefrom1166
dc.relation.ispartofpageto1173
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchCivil engineering
dc.subject.fieldofresearchcode4602
dc.subject.fieldofresearchcode4005
dc.titleCrack detection via salient structure extraction from textured background
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationNayyeri, F; Hou, L; Zhou, J; Guan, H; Liew, A, Crack detection via salient structure extraction from textured background, SHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Proceedings, 2017, pp. 1166-1173
dc.date.updated2020-01-31T03:44:49Z
gro.hasfulltextNo Full Text
gro.griffith.authorLiew, Alan Wee-Chung
gro.griffith.authorGuan, Hong
gro.griffith.authorZhou, Jun


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