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Crack Detection in Concrete Tunnels Using a Gabor Filter Invariant to Rotation
In this article, a system for the detection of cracks in concrete tunnel surfaces, based on image sensors, is presented. Both data acquisition and processing are covered. Linear cameras and proper lighting are used for data acquisition. The required resolution of the camera sensors and the number of...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5539543/ https://www.ncbi.nlm.nih.gov/pubmed/28726746 http://dx.doi.org/10.3390/s17071670 |
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author | Medina, Roberto Llamas, José Gómez-García-Bermejo, Jaime Zalama, Eduardo Segarra, Miguel José |
author_facet | Medina, Roberto Llamas, José Gómez-García-Bermejo, Jaime Zalama, Eduardo Segarra, Miguel José |
author_sort | Medina, Roberto |
collection | PubMed |
description | In this article, a system for the detection of cracks in concrete tunnel surfaces, based on image sensors, is presented. Both data acquisition and processing are covered. Linear cameras and proper lighting are used for data acquisition. The required resolution of the camera sensors and the number of cameras is discussed in terms of the crack size and the tunnel type. Data processing is done by applying a new method called Gabor filter invariant to rotation, allowing the detection of cracks in any direction. The parameter values of this filter are set by using a modified genetic algorithm based on the Differential Evolution optimization method. The detection of the pixels belonging to cracks is obtained to a balanced accuracy of 95.27%, thus improving the results of previous approaches. |
format | Online Article Text |
id | pubmed-5539543 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-55395432017-08-11 Crack Detection in Concrete Tunnels Using a Gabor Filter Invariant to Rotation Medina, Roberto Llamas, José Gómez-García-Bermejo, Jaime Zalama, Eduardo Segarra, Miguel José Sensors (Basel) Article In this article, a system for the detection of cracks in concrete tunnel surfaces, based on image sensors, is presented. Both data acquisition and processing are covered. Linear cameras and proper lighting are used for data acquisition. The required resolution of the camera sensors and the number of cameras is discussed in terms of the crack size and the tunnel type. Data processing is done by applying a new method called Gabor filter invariant to rotation, allowing the detection of cracks in any direction. The parameter values of this filter are set by using a modified genetic algorithm based on the Differential Evolution optimization method. The detection of the pixels belonging to cracks is obtained to a balanced accuracy of 95.27%, thus improving the results of previous approaches. MDPI 2017-07-20 /pmc/articles/PMC5539543/ /pubmed/28726746 http://dx.doi.org/10.3390/s17071670 Text en © 2017 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 Medina, Roberto Llamas, José Gómez-García-Bermejo, Jaime Zalama, Eduardo Segarra, Miguel José Crack Detection in Concrete Tunnels Using a Gabor Filter Invariant to Rotation |
title | Crack Detection in Concrete Tunnels Using a Gabor Filter Invariant to Rotation |
title_full | Crack Detection in Concrete Tunnels Using a Gabor Filter Invariant to Rotation |
title_fullStr | Crack Detection in Concrete Tunnels Using a Gabor Filter Invariant to Rotation |
title_full_unstemmed | Crack Detection in Concrete Tunnels Using a Gabor Filter Invariant to Rotation |
title_short | Crack Detection in Concrete Tunnels Using a Gabor Filter Invariant to Rotation |
title_sort | crack detection in concrete tunnels using a gabor filter invariant to rotation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5539543/ https://www.ncbi.nlm.nih.gov/pubmed/28726746 http://dx.doi.org/10.3390/s17071670 |
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