A Novel Computer-Vision Approach Assisted by 2D-Wavelet Transform and Locality Sensitive Discriminant Analysis for Concrete Crack Detection
department or school: Applied Science, Faculty of
department or school: Non UBC
department or school: Civil Engineering, Department of
data provider: DSpace
identifier citation: Sensors 22 (22): 8986 (2022)
creator: Gharehbaghi, Vahidreza
creator: Farsangi, Ehsan Noroozinejad
creator: Yang, Tony T. Y.
creator: Noori, Mohammad
creator: Kontoni, Denise-Penelope N.
issued: 2023-11-30T23:43:03Z
issued: 2022-11-20
description: This study proposes FastCrackNet, a computationally efficient crack-detection approach. Instead of a computationally costly convolutional neural network (CNN), this technique uses an effective, fully connected network, which is coupled with a 2D-wavelet image transform for analyzing and a locality sensitive discriminant analysis (LSDA) for reducing the number of features. The algorithm described here is used to detect tiny concrete cracks in two noisy adverse conditions and image shadows. By combining wavelet-based feature extraction, feature reduction, and a rapid classifier based on deep learning, this technique surpasses other image classifiers in terms of speed, performance, and resilience. In order to evaluate the accuracy and speed of FastCrackNet, two prominent pre-trained CNN architectures, namely GoogleNet and Xception, are employed. Findings reveal that FastCrackNet has better speed and accuracy than the other models. This study establishes performance and computational thresholds for classifying photos in difficult conditions. In terms of classification efficiency, FastCrackNet outperformed GoogleNet and the Xception model by more than 60 and 80 times, respectively. Furthermore, FastCrackNet’s dependability was proved by its robustness and stability in the presence of uncertainties produced by network characteristics and input images, such as input image size, batch size, and input image dimensions.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/86716
note: Citation: Gharehbaghi, V.;Noroozinejad Farsangi, E.; Yang, T.Y.;Noori, M.; Kontoni, D.-P.N. A NovelComputer-Vision Approach Assistedby 2D-Wavelet Transform andLocality Sensitive DiscriminantAnalysis for Concrete CrackDetection. Sensors 2022, 22, 8986.https://doi.org/10.3390/s22228986Academic Editors: Farzin Piltan andJongmyon KimReceived: 18 October 2022Accepted: 16 November 2022Published: 20 November 2022Publisher’s Note: MDPI stays neutralwith regard to jurisdictional claims inpublished maps and institutional affil-iations.Copyright: © 2022 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).sensorsArticleA Novel Computer-Vision Approach Assisted by 2D-WaveletTransform and Locality Sensitive Discriminant Analysis forConcrete Crack DetectionVahidreza Gharehbaghi 1 , Ehsan Noroozinejad Farsangi 2,3,* , T. Y. Yang 3, Mohammad Noori 4,5and Denise-Penelope N. Kontoni 6,7,*1 School of Civil Engineering, University of Kansas, Lawrence, KS 66045, USA2 Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology,Kerman 7631818356, Iran3 Department of Civil Engineering, The University of British Columbia (UBC), Vancouver, BC V6T 1Z4, Canada4 Department of Mechanical Engineering, California Polytechnic State University,San Luis Obispo, CA 93407, USA5 School of Civil Engineering, University of Leeds, Leeds LS2 9JT, UK6 Department of Civil Engineering, School of Engineering, University of the Peloponnese,GR-26334 Patras, Greece7 School of Science and Technology, Hellenic Open University, GR-26335 Patras, Greece* Correspondence: noroozinejad@kgut.ac.ir (E.N.F.); kontoni@uop.gr (D.-P.N.K.)Abstract: This study proposes FastCrackNet, a computationally efficient crack-detection approach.Instead of a computationally costly convolutional neural network (CNN), this technique uses aneffec…
has type: Article
is shown at: 10.14288/1.0437981
language: eng
peer review status: Reviewed
provider: Vancouver : University of British Columbia Library
publisher: Multidisciplinary Digital Publishing Institute
publisher DOI: 10.3390/s22228986
rights: CC BY 4.0
rights URI: https://creativecommons.org/licenses/by/4.0/
scholar level: Faculty
subject: deep learning
subject: FastCrackNet
subject: crack classification
subject: noisy data
subject: wavelet
subject: locality sensitive discriminant analysis
type: Text
identifier URI: http://hdl.handle.net/2429/86716