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Combined CNN and RNN Neural Networks for GPR Detection of Railway Subgrade Diseases
Vehicle-mounted ground-penetrating radar (GPR) has been used to non-destructively inspect and evaluate railway subgrade conditions. However, existing GPR data processing and interpretation methods mostly rely on time-consuming manual interpretation, and limited studies have applied machine learning...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10304807/ https://www.ncbi.nlm.nih.gov/pubmed/37420549 http://dx.doi.org/10.3390/s23125383 |
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author | Liu, Huan Wang, Shilei Jing, Guoqing Yu, Ziye Yang, Jin Zhang, Yong Guo, Yunlong |
author_facet | Liu, Huan Wang, Shilei Jing, Guoqing Yu, Ziye Yang, Jin Zhang, Yong Guo, Yunlong |
author_sort | Liu, Huan |
collection | PubMed |
description | Vehicle-mounted ground-penetrating radar (GPR) has been used to non-destructively inspect and evaluate railway subgrade conditions. However, existing GPR data processing and interpretation methods mostly rely on time-consuming manual interpretation, and limited studies have applied machine learning methods. GPR data are complex, high-dimensional, and redundant, in particular with non-negligible noises, for which traditional machine learning methods are not effective when applied to GPR data processing and interpretation. To solve this problem, deep learning is more suitable to process large amounts of training data, as well as to perform better data interpretation. In this study, we proposed a novel deep learning method to process GPR data, the CRNN network, which combines convolutional neural networks (CNN) and recurrent neural networks (RNN). The CNN processes raw GPR waveform data from signal channels, and the RNN processes features from multiple channels. The results show that the CRNN network achieves a higher precision at 83.4%, with a recall of 77.3%. Compared to the traditional machine learning method, the CRNN is 5.2 times faster and has a smaller size of 2.6 MB (traditional machine learning method: 104.0 MB). Our research output has demonstrated that the developed deep learning method improves the efficiency and accuracy of railway subgrade condition evaluation. |
format | Online Article Text |
id | pubmed-10304807 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103048072023-06-29 Combined CNN and RNN Neural Networks for GPR Detection of Railway Subgrade Diseases Liu, Huan Wang, Shilei Jing, Guoqing Yu, Ziye Yang, Jin Zhang, Yong Guo, Yunlong Sensors (Basel) Article Vehicle-mounted ground-penetrating radar (GPR) has been used to non-destructively inspect and evaluate railway subgrade conditions. However, existing GPR data processing and interpretation methods mostly rely on time-consuming manual interpretation, and limited studies have applied machine learning methods. GPR data are complex, high-dimensional, and redundant, in particular with non-negligible noises, for which traditional machine learning methods are not effective when applied to GPR data processing and interpretation. To solve this problem, deep learning is more suitable to process large amounts of training data, as well as to perform better data interpretation. In this study, we proposed a novel deep learning method to process GPR data, the CRNN network, which combines convolutional neural networks (CNN) and recurrent neural networks (RNN). The CNN processes raw GPR waveform data from signal channels, and the RNN processes features from multiple channels. The results show that the CRNN network achieves a higher precision at 83.4%, with a recall of 77.3%. Compared to the traditional machine learning method, the CRNN is 5.2 times faster and has a smaller size of 2.6 MB (traditional machine learning method: 104.0 MB). Our research output has demonstrated that the developed deep learning method improves the efficiency and accuracy of railway subgrade condition evaluation. MDPI 2023-06-06 /pmc/articles/PMC10304807/ /pubmed/37420549 http://dx.doi.org/10.3390/s23125383 Text en © 2023 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 Liu, Huan Wang, Shilei Jing, Guoqing Yu, Ziye Yang, Jin Zhang, Yong Guo, Yunlong Combined CNN and RNN Neural Networks for GPR Detection of Railway Subgrade Diseases |
title | Combined CNN and RNN Neural Networks for GPR Detection of Railway Subgrade Diseases |
title_full | Combined CNN and RNN Neural Networks for GPR Detection of Railway Subgrade Diseases |
title_fullStr | Combined CNN and RNN Neural Networks for GPR Detection of Railway Subgrade Diseases |
title_full_unstemmed | Combined CNN and RNN Neural Networks for GPR Detection of Railway Subgrade Diseases |
title_short | Combined CNN and RNN Neural Networks for GPR Detection of Railway Subgrade Diseases |
title_sort | combined cnn and rnn neural networks for gpr detection of railway subgrade diseases |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10304807/ https://www.ncbi.nlm.nih.gov/pubmed/37420549 http://dx.doi.org/10.3390/s23125383 |
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