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Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application

Robotic vision-based crack detection in concrete bridges is an essential task to preserve these assets and their safety. The conventional human visual inspection method is time consuming and cost inefficient. In this paper, we propose a robust algorithm to detect cracks in a pixel-wise manner from r...

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Detalles Bibliográficos
Autores principales: Li, Yundong, Li, Hongguang, Wang, Hongren
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163270/
https://www.ncbi.nlm.nih.gov/pubmed/30208665
http://dx.doi.org/10.3390/s18093042
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author Li, Yundong
Li, Hongguang
Wang, Hongren
author_facet Li, Yundong
Li, Hongguang
Wang, Hongren
author_sort Li, Yundong
collection PubMed
description Robotic vision-based crack detection in concrete bridges is an essential task to preserve these assets and their safety. The conventional human visual inspection method is time consuming and cost inefficient. In this paper, we propose a robust algorithm to detect cracks in a pixel-wise manner from real concrete surface images. In practice, crack detection remains challenging in the following aspects: (1) detection performance is disturbed by noises and clutters of environment; and (2) the requirement of high pixel-wise accuracy is difficult to obtain. To address these limitations, three steps are considered in the proposed scheme. First, a local pattern predictor (LPP) is constructed using convolutional neural networks (CNN), which can extract discriminative features of images. Second, each pixel is efficiently classified into crack categories or non-crack categories by LPP, using as context a patch centered on the pixel. Lastly, the output of CNN—i.e., confidence map—is post-processed to obtain the crack areas. We evaluate the proposed algorithm on samples captured from several concrete bridges. The experimental results demonstrate the good performance of the proposed method.
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spelling pubmed-61632702018-10-10 Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application Li, Yundong Li, Hongguang Wang, Hongren Sensors (Basel) Article Robotic vision-based crack detection in concrete bridges is an essential task to preserve these assets and their safety. The conventional human visual inspection method is time consuming and cost inefficient. In this paper, we propose a robust algorithm to detect cracks in a pixel-wise manner from real concrete surface images. In practice, crack detection remains challenging in the following aspects: (1) detection performance is disturbed by noises and clutters of environment; and (2) the requirement of high pixel-wise accuracy is difficult to obtain. To address these limitations, three steps are considered in the proposed scheme. First, a local pattern predictor (LPP) is constructed using convolutional neural networks (CNN), which can extract discriminative features of images. Second, each pixel is efficiently classified into crack categories or non-crack categories by LPP, using as context a patch centered on the pixel. Lastly, the output of CNN—i.e., confidence map—is post-processed to obtain the crack areas. We evaluate the proposed algorithm on samples captured from several concrete bridges. The experimental results demonstrate the good performance of the proposed method. MDPI 2018-09-11 /pmc/articles/PMC6163270/ /pubmed/30208665 http://dx.doi.org/10.3390/s18093042 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Yundong
Li, Hongguang
Wang, Hongren
Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application
title Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application
title_full Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application
title_fullStr Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application
title_full_unstemmed Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application
title_short Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application
title_sort pixel-wise crack detection using deep local pattern predictor for robot application
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163270/
https://www.ncbi.nlm.nih.gov/pubmed/30208665
http://dx.doi.org/10.3390/s18093042
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AT wanghongren pixelwisecrackdetectionusingdeeplocalpatternpredictorforrobotapplication