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Deep Learning-Based Concrete Surface Damage Monitoring Method Using Structured Lights and Depth Camera
Due to the increase in aging structures and the decrease in construction workforce, there is an increasing interest in automating structural damage monitoring. Surface damage on concrete structures, such as cracks, delamination, and rebar exposure, is one of the important parameters that can be used...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070667/ https://www.ncbi.nlm.nih.gov/pubmed/33919733 http://dx.doi.org/10.3390/s21082759 |
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author | Bang, Hyuntae Min, Jiyoung Jeon, Haemin |
author_facet | Bang, Hyuntae Min, Jiyoung Jeon, Haemin |
author_sort | Bang, Hyuntae |
collection | PubMed |
description | Due to the increase in aging structures and the decrease in construction workforce, there is an increasing interest in automating structural damage monitoring. Surface damage on concrete structures, such as cracks, delamination, and rebar exposure, is one of the important parameters that can be used to estimate the condition of the structure. In this paper, deep learning-based detection and quantification of structural damage using structured lights and a depth camera is proposed. The proposed monitoring system is composed of four lasers and a depth camera. The lasers are projected on the surface of the structures, and the camera captures images of the structures while measuring distance. By calculating an image homography, the captured images are calibrated when the structure and sensing system are not in parallel. The Faster RCNN (Region-based Convolutional Neural Network) with Inception Resnet v2 architecture is used to detect three types of surface damage: (i) cracks; (ii) delamination; and (iii) rebar exposure. The detected damage is quantified by calculating the positions of the projected laser beams with the measured distance. The experimental results show that structural damage was detected with an F1 score of 0.83 and a median value of the quantified relative error of less than 5%. |
format | Online Article Text |
id | pubmed-8070667 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80706672021-04-26 Deep Learning-Based Concrete Surface Damage Monitoring Method Using Structured Lights and Depth Camera Bang, Hyuntae Min, Jiyoung Jeon, Haemin Sensors (Basel) Article Due to the increase in aging structures and the decrease in construction workforce, there is an increasing interest in automating structural damage monitoring. Surface damage on concrete structures, such as cracks, delamination, and rebar exposure, is one of the important parameters that can be used to estimate the condition of the structure. In this paper, deep learning-based detection and quantification of structural damage using structured lights and a depth camera is proposed. The proposed monitoring system is composed of four lasers and a depth camera. The lasers are projected on the surface of the structures, and the camera captures images of the structures while measuring distance. By calculating an image homography, the captured images are calibrated when the structure and sensing system are not in parallel. The Faster RCNN (Region-based Convolutional Neural Network) with Inception Resnet v2 architecture is used to detect three types of surface damage: (i) cracks; (ii) delamination; and (iii) rebar exposure. The detected damage is quantified by calculating the positions of the projected laser beams with the measured distance. The experimental results show that structural damage was detected with an F1 score of 0.83 and a median value of the quantified relative error of less than 5%. MDPI 2021-04-14 /pmc/articles/PMC8070667/ /pubmed/33919733 http://dx.doi.org/10.3390/s21082759 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bang, Hyuntae Min, Jiyoung Jeon, Haemin Deep Learning-Based Concrete Surface Damage Monitoring Method Using Structured Lights and Depth Camera |
title | Deep Learning-Based Concrete Surface Damage Monitoring Method Using Structured Lights and Depth Camera |
title_full | Deep Learning-Based Concrete Surface Damage Monitoring Method Using Structured Lights and Depth Camera |
title_fullStr | Deep Learning-Based Concrete Surface Damage Monitoring Method Using Structured Lights and Depth Camera |
title_full_unstemmed | Deep Learning-Based Concrete Surface Damage Monitoring Method Using Structured Lights and Depth Camera |
title_short | Deep Learning-Based Concrete Surface Damage Monitoring Method Using Structured Lights and Depth Camera |
title_sort | deep learning-based concrete surface damage monitoring method using structured lights and depth camera |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070667/ https://www.ncbi.nlm.nih.gov/pubmed/33919733 http://dx.doi.org/10.3390/s21082759 |
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