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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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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. |
format | Online Article Text |
id | pubmed-6163270 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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