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Crack detection using fusion features-based broad learning system and image processing
Zhang, Yang1,2,3; Yuen, Ka Veng1,2
2021-12-01
Source PublicationCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
ISSN1093-9687
Volume36Issue:12Pages:1568-1584
Abstract

Deep learning has been widely applied to vision-based structural damage detection, but its computational demand is high. To avoid this computational burden, a novel crack detection system, namely, fusion features-based broad learning system (FF-BLS), is proposed for efficient training without GPU acceleration. In FF-BLS, a convolution module with fixed weights is used to extract the fusion features of images. Feature nodes and enhancement nodes randomly generated by fusion features are used to estimate the output of the network. Meanwhile, the proposed FF-BLS is a dynamical system, which achieves incremental learning by adding nodes. Thus, the trained FF-BLS model can be updated efficiently with additional data, and this substantially reduces the training cost. Finally, FF-BLS was applied to crack detection. Compared with some well-known deep convolutional neural networks (VGG16, ResNet50, InceptionV3, Xception, and EfficientNet), the FF-BLS achieved a similar level of recognition accuracy, but the training speed was increased by more than 20 times.

DOI10.1111/mice.12753
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Construction & Building Technology ; Engineering ; Transportation
WOS SubjectComputer Science, Interdisciplinary Applications ; Construction & Building Technology ; Engineering, Civil ; Transportation Science & Technology
WOS IDWOS:000692092400001
Scopus ID2-s2.0-85114116174
Fulltext Access
FWCI6.5653043
Citation statistics
Document TypeJournal article
CollectionTHE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
Faculty of Science and Technology
Corresponding AuthorYuen, Ka Veng
Affiliation1.State Key Laboratory of Internet ofThings for Smart City and Department ofCivil and Environmental Engineering,University of Macau, Macau, China
2.Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, University of Macau, Macao
3.Guangxi Key Laboratory of Disaster Prevention and Engineering Safety, Guangxi University, Guangxi, China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Zhang, Yang,Yuen, Ka Veng. Crack detection using fusion features-based broad learning system and image processing[J]. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING,2021,36(12):1568-1584.
APA Zhang, Yang,&Yuen, Ka Veng.(2021).Crack detection using fusion features-based broad learning system and image processing.COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING,36(12),1568-1584.
MLA Zhang, Yang,et al."Crack detection using fusion features-based broad learning system and image processing".COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING 36.12(2021):1568-1584.
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