{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T22:57:42Z","timestamp":1771455462153,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,20]],"date-time":"2022-11-20T00:00:00Z","timestamp":1668902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s22228986","type":"journal-article","created":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T04:39:59Z","timestamp":1669005599000},"page":"8986","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Novel Computer-Vision Approach Assisted by 2D-Wavelet Transform and Locality Sensitive Discriminant Analysis for Concrete Crack Detection"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6831-313X","authenticated-orcid":false,"given":"Vahidreza","family":"Gharehbaghi","sequence":"first","affiliation":[{"name":"School of Civil Engineering, University of Kansas, Lawrence, KS 66045, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2790-526X","authenticated-orcid":false,"given":"Ehsan","family":"Noroozinejad Farsangi","sequence":"additional","affiliation":[{"name":"Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman 7631818356, Iran"},{"name":"Department of Civil Engineering, The University of British Columbia (UBC), Vancouver, BC V6T 1Z4, Canada"}]},{"given":"T. Y.","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, The University of British Columbia (UBC), Vancouver, BC V6T 1Z4, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2793-5194","authenticated-orcid":false,"given":"Mohammad","family":"Noori","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA"},{"name":"School of Civil Engineering, University of Leeds, Leeds LS2 9JT, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4844-1094","authenticated-orcid":false,"given":"Denise-Penelope N.","family":"Kontoni","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, School of Engineering, University of the Peloponnese, GR-26334 Patras, Greece"},{"name":"School of Science and Technology, Hellenic Open University, GR-26335 Patras, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.eswa.2017.05.039","article-title":"Deep learning for biological image classification","volume":"85","author":"Affonso","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1061\/(ASCE)BE.1943-5592.0000090","article-title":"I-35W bridge collapse","volume":"15","author":"Hao","year":"2010","journal-title":"J. Bridge Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1007\/s40999-018-0332-x","article-title":"Fire structural response of the plasco building: A preliminary investigation report","volume":"17","author":"Behnam","year":"2019","journal-title":"Int. J. Civ. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gharehbaghi, V.R., Noroozinejad Farsangi, E., Noori, M., Yang, T., Li, S., Nguyen, A., M\u00e1laga-Chuquitaype, C., Gardoni, P., and Mirjalili, S. (2021). A critical review on structural health monitoring: Definitions, methods, and perspectives. Arch. Comput. Methods Eng., 1\u201327.","DOI":"10.1007\/s11831-021-09665-9"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.eng.2018.11.030","article-title":"Advances in computer vision-based civil infrastructure inspection and monitoring","volume":"5","author":"Spencer","year":"2019","journal-title":"Engineering."},{"key":"ref_6","first-page":"37","article-title":"Long short-term memory","volume":"385","author":"Graves","year":"2012","journal-title":"Supervised Seq. Label. Recurr. Neural Netw."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5947","DOI":"10.4249\/scholarpedia.5947","article-title":"Deep belief networks","volume":"4","author":"Hinton","year":"2009","journal-title":"Scholarpedia"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A. (2008, January 5\u20139). Extracting and composing robust features with denoising autoencoders. Proceedings of the 25th international Conference on Machine learning, Helsinki, Finland.","DOI":"10.1145\/1390156.1390294"},{"key":"ref_10","first-page":"64","article-title":"Recurrent neural networks","volume":"5","author":"Medsker","year":"2001","journal-title":"Des. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s00138-009-0244-5","article-title":"A robust automatic crack detection method from noisy concrete surfaces","volume":"22","author":"Fujita","year":"2011","journal-title":"Mach. Vis. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2352\/J.ImagingSci.Technol.2020.64.2.020508","article-title":"Medical image segmentation based on u-net: A review","volume":"64","author":"Du","year":"2020","journal-title":"J. Imaging Sci. Technol."},{"key":"ref_14","unstructured":"Jenkins, M.D., Carr, T.A., Iglesias, M.I., Buggy, T., and Morison, G. (2018, January 3\u20137). A deep convolutional neural network for semantic pixel-wise segmentation of road and pavement surface cracks. Proceedings of the 2018 26th European signal processing Conference (EUSIPCO), Rome, Italy."},{"key":"ref_15","first-page":"3523","article-title":"Image segmentation using deep learning: A survey","volume":"44","author":"Minaee","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Galvez, R.L., Bandala, A.A., Dadios, E.P., Vicerra, R.R.P., and Maningo, J.M.Z. (2018, January 28\u201331). Object detection using convolutional neural networks. Proceedings of the TENCON 2018-2018 IEEE Region 10 Conference, Jeju, Republic of Korea.","DOI":"10.1109\/TENCON.2018.8650517"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neucom.2020.01.085","article-title":"Recent advances in deep learning for object detection","volume":"396","author":"Wu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","article-title":"Object detection with deep learning: A review","volume":"30","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhu, J., and Song, J. (2020). An intelligent classification model for surface defects on cement concrete bridges. Appl. Sci., 10.","DOI":"10.3390\/app10030972"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3295748","article-title":"A comprehensive survey of deep learning for image captioning","volume":"51","author":"Hossain","year":"2019","journal-title":"ACM Comput. Surv. (CsUR)"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, C., Yang, H., Bartz, C., and Meinel, C. (2016, January 15\u201319). Image captioning with deep bidirectional LSTMs. Proceedings of the 24th ACM International Conference on Multimedia, Amsterdam, The Netherlands.","DOI":"10.1145\/2964284.2964299"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.autcon.2018.11.028","article-title":"Autonomous concrete crack detection using deep fully convolutional neural network","volume":"99","author":"Dung","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Islam, M.M., Hossain, M.B., Akhtar, M.N., Moni, M.A., and Hasan, K.F. (2022). CNN Based on Transfer Learning Models Using Data Augmentation and Transformation for Detection of Concrete Crack. Algorithms, 15.","DOI":"10.3390\/a15080287"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1080\/14680629.2021.1925578","article-title":"A real-time crack detection algorithm for pavement based on CNN with multiple feature layers","volume":"23","author":"Ma","year":"2022","journal-title":"Road Mater. Pavement Des."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.ijtst.2021.04.008","article-title":"Pavement distress detection using convolutional neural network (CNN): A case study in Montreal, Canada","volume":"11","author":"Zhang","year":"2022","journal-title":"Int. J. Transp. Sci. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e2766","DOI":"10.1002\/stc.2766","article-title":"Surface crack detection based on image stitching and transfer learning with pretrained convolutional neural network","volume":"28","author":"Wu","year":"2021","journal-title":"Struct. Control. Health Monit."},{"key":"ref_28","unstructured":"Chianese, R., Nguyen, A., Gharehbaghi, V., Aravinthan, T., and Noori, M. (2021). Influence of image noise on crack detection performance of deep convolutional neural networks. arXiv, Available online: https:\/\/arxiv.org\/abs\/2111.02079."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1109\/TASSP.1976.1162805","article-title":"A new principle for fast Fourier transformation","volume":"24","author":"Rader","year":"1976","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1109\/TASSP.1984.1164317","article-title":"Signal estimation from modified short-time Fourier transform","volume":"32","author":"Griffin","year":"1984","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_31","unstructured":"Johansson, M., and The Hilbert Transform (2011, December 31). Master\u2019s Thesis. V\u00e4xj\u00f6 University, Suecia. Available online: http:\/\/w3.msi.vxu.se\/exarb\/mj_ex.pdf."},{"key":"ref_32","first-page":"136","article-title":"A novel approach for deterioration and damage identification in building structures based on Stockwell-Transform and deep convolutional neural network","volume":"7","author":"Gharehbaghi","year":"2022","journal-title":"J. Struct. Integr. Maint."},{"key":"ref_33","first-page":"203","article-title":"A comprehensive study on wavelet based shrinkage methods for denoising natural images","volume":"9","author":"Sutha","year":"2013","journal-title":"WSEAS Trans. Signal Process."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.fcij.2017.10.005","article-title":"Wavelet based transition region extraction for image segmentation","volume":"2","author":"Parida","year":"2017","journal-title":"Future Comput. Inform. J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.protcy.2012.10.041","article-title":"DWT based feature extraction using edge tracked scale normalization for enhanced face recognition","volume":"6","author":"Rinky","year":"2012","journal-title":"Procedia Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.1049\/iet-ipr.2017.1305","article-title":"Daubechies wavelet-based local feature descriptor for multimodal medical image registration","volume":"12","author":"Yelampalli","year":"2018","journal-title":"IET Image Process."},{"key":"ref_37","unstructured":"Cai, D., He, X., Zhou, K., Han, J., and Bao, H. (2007, January 6\u201312). Locality sensitive discriminant analysis. Proceedings of the IJCAI, Hyderabad, India."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.jsv.2017.02.041","article-title":"A new time\u2013frequency method for identification and classification of ball bearing faults","volume":"397","author":"Attoui","year":"2017","journal-title":"J. Sound Vib."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Guan, H., and Chan, T.H.T. (2022). Robustness of Deep Transfer Learning-Based Crack Detection against Uncertainty in Hyperparameter Tuning and Input Data. Recent Advances in Structural Health Monitoring Research in Australia, Nova Science Publishers. Civil Engineering and Architecture.","DOI":"10.52305\/QHVI3457"},{"key":"ref_41","unstructured":"(2022, October 18). 3000_ImageData_for_Crack_Detection; Kaggle. Available online: https:\/\/www.kaggle.com\/datasets\/nguyen49\/3000-imagedata-for-crack-detection."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.engstruct.2017.10.070","article-title":"A novel unsupervised deep learning model for global and local health condition assessment of structures","volume":"156","author":"Rafiei","year":"2018","journal-title":"Eng. Struct."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"111328","DOI":"10.1016\/j.nucengdes.2021.111328","article-title":"Evaluation of ASR in concrete using acoustic emission and deep learning","volume":"380","author":"Ai","year":"2021","journal-title":"Nucl. Eng. Des."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.istruc.2020.11.040","article-title":"Deterioration and damage identification in building structures using a novel feature selection method","volume":"29","author":"Gharehbaghi","year":"2021","journal-title":"Structures"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Dixit, A., and Wagatsuma, H. (2018, January 7\u201310). Comparison of effectiveness of dual tree complex wavelet transform and anisotropic diffusion in MCA for concrete crack detection. Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan.","DOI":"10.1109\/SMC.2018.00458"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1007\/s42947-020-0098-9","article-title":"An image-based system for pavement crack evaluation using transfer learning and wavelet transform","volume":"14","author":"Ranjbar","year":"2021","journal-title":"Int. J. Pavement Res. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"33","DOI":"10.3221\/IGF-ESIS.58.03","article-title":"Wavelet-based multiresolution analysis coupled with deep learning to efficiently monitor cracks in concrete","volume":"15","author":"Arbaoui","year":"2021","journal-title":"Frat. Ed Integrit\u00e0 Strutt."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Arbaoui, A., Ouahabi, A., Jacques, S., and Hamiane, M. (2021). Concrete cracks detection and monitoring using deep learning-based multiresolution analysis. Electronics, 10.","DOI":"10.20944\/preprints202106.0194.v1"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Yang, G., Geng, P., Ma, H., Liu, J., and Luo, J. (2021, January 5\u20137). DWTA-Unet: Concrete Crack Segmentation Based on Discrete Wavelet Transform and Unet. Proceedings of the 2021 Chinese Intelligent Automation Conference, Zhanjiang, China.","DOI":"10.1007\/978-981-16-6372-7_75"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.icte.2020.04.010","article-title":"The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset","volume":"6","author":"Kandel","year":"2020","journal-title":"ICT Express"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8986\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:22:21Z","timestamp":1760145741000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8986"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,20]]},"references-count":50,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22228986"],"URL":"https:\/\/doi.org\/10.3390\/s22228986","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,20]]}}}