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Monitoring of Corroded and Loosened Bolts in Steel Structures via Deep Learning and Hough Transforms
In this study, a regional convolutional neural network (RCNN)-based deep learning and Hough line transform (HLT) algorithm are applied to monitor corroded and loosened bolts in steel structures. The monitoring goals are to detect rusted bolts distinguished from non-corroded ones and also to estimate...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7731320/ https://www.ncbi.nlm.nih.gov/pubmed/33276512 http://dx.doi.org/10.3390/s20236888 |
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author | Ta, Quoc-Bao Kim, Jeong-Tae |
author_facet | Ta, Quoc-Bao Kim, Jeong-Tae |
author_sort | Ta, Quoc-Bao |
collection | PubMed |
description | In this study, a regional convolutional neural network (RCNN)-based deep learning and Hough line transform (HLT) algorithm are applied to monitor corroded and loosened bolts in steel structures. The monitoring goals are to detect rusted bolts distinguished from non-corroded ones and also to estimate bolt-loosening angles of the identified bolts. The following approaches are performed to achieve the goals. Firstly, a RCNN-based autonomous bolt detection scheme is designed to identify corroded and clean bolts in a captured image. Secondly, a HLT-based image processing algorithm is designed to estimate rotational angles (i.e., bolt-loosening) of cropped bolts. Finally, the accuracy of the proposed framework is experimentally evaluated under various capture distances, perspective distortions, and light intensities. The lab-scale monitoring results indicate that the suggested method accurately acquires rusted bolts for images captured under perspective distortion angles less than 15° and light intensities larger than 63 lux. |
format | Online Article Text |
id | pubmed-7731320 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77313202020-12-12 Monitoring of Corroded and Loosened Bolts in Steel Structures via Deep Learning and Hough Transforms Ta, Quoc-Bao Kim, Jeong-Tae Sensors (Basel) Article In this study, a regional convolutional neural network (RCNN)-based deep learning and Hough line transform (HLT) algorithm are applied to monitor corroded and loosened bolts in steel structures. The monitoring goals are to detect rusted bolts distinguished from non-corroded ones and also to estimate bolt-loosening angles of the identified bolts. The following approaches are performed to achieve the goals. Firstly, a RCNN-based autonomous bolt detection scheme is designed to identify corroded and clean bolts in a captured image. Secondly, a HLT-based image processing algorithm is designed to estimate rotational angles (i.e., bolt-loosening) of cropped bolts. Finally, the accuracy of the proposed framework is experimentally evaluated under various capture distances, perspective distortions, and light intensities. The lab-scale monitoring results indicate that the suggested method accurately acquires rusted bolts for images captured under perspective distortion angles less than 15° and light intensities larger than 63 lux. MDPI 2020-12-02 /pmc/articles/PMC7731320/ /pubmed/33276512 http://dx.doi.org/10.3390/s20236888 Text en © 2020 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 Ta, Quoc-Bao Kim, Jeong-Tae Monitoring of Corroded and Loosened Bolts in Steel Structures via Deep Learning and Hough Transforms |
title | Monitoring of Corroded and Loosened Bolts in Steel Structures via Deep Learning and Hough Transforms |
title_full | Monitoring of Corroded and Loosened Bolts in Steel Structures via Deep Learning and Hough Transforms |
title_fullStr | Monitoring of Corroded and Loosened Bolts in Steel Structures via Deep Learning and Hough Transforms |
title_full_unstemmed | Monitoring of Corroded and Loosened Bolts in Steel Structures via Deep Learning and Hough Transforms |
title_short | Monitoring of Corroded and Loosened Bolts in Steel Structures via Deep Learning and Hough Transforms |
title_sort | monitoring of corroded and loosened bolts in steel structures via deep learning and hough transforms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7731320/ https://www.ncbi.nlm.nih.gov/pubmed/33276512 http://dx.doi.org/10.3390/s20236888 |
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