Vision-based concrete crack detection using a hybrid framework considering noise effect

Yu, Y; Samali, B; Rashidi, M; Mohammadi, M; Nguyen, TN; Zhang, G

Vision-based concrete crack detection using a hybrid framework considering noise effect

Yu, Y

Samali, B Rashidi, M Mohammadi, M Nguyen, TN Zhang, G

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Journal of Building Engineering, 2022, 61
Issue Date:: 2022-12-01

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Field	Value	Language
dc.contributor.author	Yu, Y https://orcid.org/0000-0001-9592-8191
dc.contributor.author	Samali, B
dc.contributor.author	Rashidi, M
dc.contributor.author	Mohammadi, M
dc.contributor.author	Nguyen, TN
dc.contributor.author	Zhang, G
dc.date.accessioned	2023-01-24T00:15:47Z
dc.date.available	2023-01-24T00:15:47Z
dc.date.issued	2022-12-01
dc.identifier.citation	Journal of Building Engineering, 2022, 61
dc.identifier.issn	2352-7102
dc.identifier.issn	2352-7102
dc.identifier.uri	http://hdl.handle.net/10453/165384
dc.description.abstract	Diagnosing surface cracks of concrete structures has been a critical aspect of assessing structural integrity. Existing diagnosis technologies are time-consuming, subjective, and heavily dependent on the experiences of inspectors, which leads to low detection accuracy. This paper aims to resolve these challenges by proposing a vision-based automated method for surface condition identification of concrete structures, consisting of state-of-the-art pre-trained convolutional neural networks (CNNs), transfer learning, and decision-level image fusion. For this purpose, a total of 41,780 image patches of various concrete surfaces are generated for the development and validation of the proposed method. Each pre-trained CNN is employed to establish the predictive model for the initial diagnosis of surface conditions via transfer learning. Since different CNNs may generate conflicting results due to differences in network architectures, a modified Dempster-Shafer (D-S) algorithm is designed to conduct decision-level image fusion to improve the crack detection accuracy. The superiority of the proposed method is validated via the comparison with single CNN models. The robustness of the proposed method is also verified using the images polluted with various types and intensities of noise, with satisfactory outcomes. Finally, this hybridised approach is applied to the analysis of images of concrete structures captured in the field, through an exhaust search-based scanning window. The results show that it is capable of accurately identifying the crack profile with wrong predictions of limited areas, demonstrating its potential in practical applications.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Journal of Building Engineering
dc.relation.isbasedon	10.1016/j.jobe.2022.105246
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0905 Civil Engineering, 1201 Architecture, 1202 Building
dc.title	Vision-based concrete crack detection using a hybrid framework considering noise effect
dc.type	Journal Article
utslib.citation.volume	61
utslib.for	0905 Civil Engineering
utslib.for	1201 Architecture
utslib.for	1202 Building
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 Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-01-24T00:15:19Z
pubs.publication-status	Published
pubs.volume	61

Abstract:

Diagnosing surface cracks of concrete structures has been a critical aspect of assessing structural integrity. Existing diagnosis technologies are time-consuming, subjective, and heavily dependent on the experiences of inspectors, which leads to low detection accuracy. This paper aims to resolve these challenges by proposing a vision-based automated method for surface condition identification of concrete structures, consisting of state-of-the-art pre-trained convolutional neural networks (CNNs), transfer learning, and decision-level image fusion. For this purpose, a total of 41,780 image patches of various concrete surfaces are generated for the development and validation of the proposed method. Each pre-trained CNN is employed to establish the predictive model for the initial diagnosis of surface conditions via transfer learning. Since different CNNs may generate conflicting results due to differences in network architectures, a modified Dempster-Shafer (D-S) algorithm is designed to conduct decision-level image fusion to improve the crack detection accuracy. The superiority of the proposed method is validated via the comparison with single CNN models. The robustness of the proposed method is also verified using the images polluted with various types and intensities of noise, with satisfactory outcomes. Finally, this hybridised approach is applied to the analysis of images of concrete structures captured in the field, through an exhaust search-based scanning window. The results show that it is capable of accurately identifying the crack profile with wrong predictions of limited areas, demonstrating its potential in practical applications.

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http://hdl.handle.net/10453/165384