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Crack detection for concrete bridges with imaged based deep learning

Within the framework of intelligent bridge detection, a number of crack detection methods based on image processing techniques have been implemented. In this study, a combined novel approach with deep learning of a single shot multibox detector (SSD) and the eight neighborhood algorithm is proposed...

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Autores principales: Wan, Chunfeng, Xiong, Xiaobing, Wen, Bo, Gao, Shuai, Fang, Da, Yang, Caiqian, Xue, Songtao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10450596/
https://www.ncbi.nlm.nih.gov/pubmed/36177737
http://dx.doi.org/10.1177/00368504221128487
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author Wan, Chunfeng
Xiong, Xiaobing
Wen, Bo
Gao, Shuai
Fang, Da
Yang, Caiqian
Xue, Songtao
author_facet Wan, Chunfeng
Xiong, Xiaobing
Wen, Bo
Gao, Shuai
Fang, Da
Yang, Caiqian
Xue, Songtao
author_sort Wan, Chunfeng
collection PubMed
description Within the framework of intelligent bridge detection, a number of crack detection methods based on image processing techniques have been implemented. In this study, a combined novel approach with deep learning of a single shot multibox detector (SSD) and the eight neighborhood algorithm is proposed and applied to bridge crack image identification to provide an automatic method for crack detection. First, a large number of concrete crack images collected from the site were segmented and preprocessed for the establishment of a crack image dataset. Deep learning of the SSD algorithm was introduced on the training set to establish the detection model, where the model parameters were adjusted by the validation set. Sliding window technology was integrated to identify the cracks in the test set. The effects of the sliding window size and dataset size on the crack detection results were discussed. Moreover, the eight neighborhood algorithm was adopted for further crack detection correction. The results show that the configuration achieves good crack detection by the deep learning of the SSD algorithm with high precision and recall. The introduction of the eight neighborhood correction algorithm further improves the detection results by eliminating some misjudged results. Finally, the developed algorithm was placed into a portable device, with which cracks were effectively identified. The introduced method shows significantly better performance in crack detection, and the system installed on the portable device provides a way to broaden its application in the automatic crack detection of concrete bridges.
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spelling pubmed-104505962023-08-26 Crack detection for concrete bridges with imaged based deep learning Wan, Chunfeng Xiong, Xiaobing Wen, Bo Gao, Shuai Fang, Da Yang, Caiqian Xue, Songtao Sci Prog Original Manuscript Within the framework of intelligent bridge detection, a number of crack detection methods based on image processing techniques have been implemented. In this study, a combined novel approach with deep learning of a single shot multibox detector (SSD) and the eight neighborhood algorithm is proposed and applied to bridge crack image identification to provide an automatic method for crack detection. First, a large number of concrete crack images collected from the site were segmented and preprocessed for the establishment of a crack image dataset. Deep learning of the SSD algorithm was introduced on the training set to establish the detection model, where the model parameters were adjusted by the validation set. Sliding window technology was integrated to identify the cracks in the test set. The effects of the sliding window size and dataset size on the crack detection results were discussed. Moreover, the eight neighborhood algorithm was adopted for further crack detection correction. The results show that the configuration achieves good crack detection by the deep learning of the SSD algorithm with high precision and recall. The introduction of the eight neighborhood correction algorithm further improves the detection results by eliminating some misjudged results. Finally, the developed algorithm was placed into a portable device, with which cracks were effectively identified. The introduced method shows significantly better performance in crack detection, and the system installed on the portable device provides a way to broaden its application in the automatic crack detection of concrete bridges. SAGE Publications 2022-09-30 /pmc/articles/PMC10450596/ /pubmed/36177737 http://dx.doi.org/10.1177/00368504221128487 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Manuscript
Wan, Chunfeng
Xiong, Xiaobing
Wen, Bo
Gao, Shuai
Fang, Da
Yang, Caiqian
Xue, Songtao
Crack detection for concrete bridges with imaged based deep learning
title Crack detection for concrete bridges with imaged based deep learning
title_full Crack detection for concrete bridges with imaged based deep learning
title_fullStr Crack detection for concrete bridges with imaged based deep learning
title_full_unstemmed Crack detection for concrete bridges with imaged based deep learning
title_short Crack detection for concrete bridges with imaged based deep learning
title_sort crack detection for concrete bridges with imaged based deep learning
topic Original Manuscript
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10450596/
https://www.ncbi.nlm.nih.gov/pubmed/36177737
http://dx.doi.org/10.1177/00368504221128487
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