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Intelligent recognition of defects in high‐speed railway slab track with limited dataset

Xiaopei Cai, Xueyang Tang, Shuo Pan, Yi Wang, Hai Yan, Yuheng Ren, Ning Chen, Yue Hou Orcid Logo

Computer-Aided Civil and Infrastructure Engineering, Volume: 39, Issue: 6, Pages: 911 - 928

Swansea University Author: Yue Hou Orcid Logo

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DOI (Published version): 10.1111/mice.13109

Abstract

During the regular service life of high-speed railway (HSR), there might be serious defects in the concrete slabs of the infrastructure systems, which may further significantly affect public transportation safety. To address these serious issues and fulfill the regular functions of HSR, the traditio...

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Published in: Computer-Aided Civil and Infrastructure Engineering
ISSN: 1093-9687 1467-8667
Published: Wiley 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa64680
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To address these serious issues and fulfill the regular functions of HSR, the traditional methods for railway engineers involve carrying out regular on-site inspections manually or by semi-automatic inspection vehicles, and conducting timely corresponding repairing approaches and maintenance, where these methods are time-consuming and dangerous. In recent years, machine learning methods have been widely applied to the intelligent and automatic detection of severe defects in HSR. Currently, one of the most serious problems is the lack of sufficient high-quality data for model training, resulting in low recognition accuracy in HSR defects. To solve this problem, this paper proposed an intelligent recognition of defects in concrete slabs of HSR based on a few-shot learning model, that is, an artificial intelligence model based on limited data size, which recognizes three service conditions of concrete slabs in HSR: cracks, track board gaps, and unbroken state. Lightweight few-shot learning models specifically designed for HSR detection were proposed. Experiments were conducted to compare the performances of different lightweight-designed models, including accuracy, parameter quantity, and testing time. Results showed that the optimum model can fast and satisfactorily recognize the defects in HSR with a very limited data size of 10 samples for each training category, with a satisfactory accuracy of 73.9% in the test dataset with 20 samples for each category, parameter amounts of 2.8 million, and a testing time of 2.2 s per image. 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spelling v2 64680 2023-10-09 Intelligent recognition of defects in high‐speed railway slab track with limited dataset 92bf566c65343cb3ee04ad963eacf31b 0000-0002-4334-2620 Yue Hou Yue Hou true false 2023-10-09 ACEM During the regular service life of high-speed railway (HSR), there might be serious defects in the concrete slabs of the infrastructure systems, which may further significantly affect public transportation safety. To address these serious issues and fulfill the regular functions of HSR, the traditional methods for railway engineers involve carrying out regular on-site inspections manually or by semi-automatic inspection vehicles, and conducting timely corresponding repairing approaches and maintenance, where these methods are time-consuming and dangerous. In recent years, machine learning methods have been widely applied to the intelligent and automatic detection of severe defects in HSR. Currently, one of the most serious problems is the lack of sufficient high-quality data for model training, resulting in low recognition accuracy in HSR defects. To solve this problem, this paper proposed an intelligent recognition of defects in concrete slabs of HSR based on a few-shot learning model, that is, an artificial intelligence model based on limited data size, which recognizes three service conditions of concrete slabs in HSR: cracks, track board gaps, and unbroken state. Lightweight few-shot learning models specifically designed for HSR detection were proposed. Experiments were conducted to compare the performances of different lightweight-designed models, including accuracy, parameter quantity, and testing time. Results showed that the optimum model can fast and satisfactorily recognize the defects in HSR with a very limited data size of 10 samples for each training category, with a satisfactory accuracy of 73.9% in the test dataset with 20 samples for each category, parameter amounts of 2.8 million, and a testing time of 2.2 s per image. This study provides a reference for the automatic recognition of defects in HSR by railway engineers with insufficient samples. Journal Article Computer-Aided Civil and Infrastructure Engineering 39 6 911 928 Wiley 1093-9687 1467-8667 15 3 2024 2024-03-15 10.1111/mice.13109 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University This research is supported by the National Natural Science Foundation of China No. 52178405, Project of Science and Technology Research and Development Program of China State Railway Group Co., Ltd. No. SY2022T002; Open Fund of National Key Laboratory of High-speed Railway Track Technology No. 2021YJ053. 2024-09-17T16:24:40.2736615 2023-10-09T15:35:55.6230931 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Xiaopei Cai 1 Xueyang Tang 2 Shuo Pan 3 Yi Wang 4 Hai Yan 5 Yuheng Ren 6 Ning Chen 7 Yue Hou 0000-0002-4334-2620 8 64680__28950__e073bd01c1694ed5b11fa08c6f5c82d0.pdf 64680.VOR.pdf 2023-11-07T14:08:06.4998947 Output 2999932 application/pdf Version of Record true © 2023 The Authors. Computer-Aided Civil and Infrastructure Engineering published by Wiley Periodicals LLC on behalf of Editor. Distributed under the terms of a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title Intelligent recognition of defects in high‐speed railway slab track with limited dataset
spellingShingle Intelligent recognition of defects in high‐speed railway slab track with limited dataset
Yue Hou
title_short Intelligent recognition of defects in high‐speed railway slab track with limited dataset
title_full Intelligent recognition of defects in high‐speed railway slab track with limited dataset
title_fullStr Intelligent recognition of defects in high‐speed railway slab track with limited dataset
title_full_unstemmed Intelligent recognition of defects in high‐speed railway slab track with limited dataset
title_sort Intelligent recognition of defects in high‐speed railway slab track with limited dataset
author_id_str_mv 92bf566c65343cb3ee04ad963eacf31b
author_id_fullname_str_mv 92bf566c65343cb3ee04ad963eacf31b_***_Yue Hou
author Yue Hou
author2 Xiaopei Cai
Xueyang Tang
Shuo Pan
Yi Wang
Hai Yan
Yuheng Ren
Ning Chen
Yue Hou
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container_volume 39
container_issue 6
container_start_page 911
publishDate 2024
institution Swansea University
issn 1093-9687
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doi_str_mv 10.1111/mice.13109
publisher Wiley
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
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description During the regular service life of high-speed railway (HSR), there might be serious defects in the concrete slabs of the infrastructure systems, which may further significantly affect public transportation safety. To address these serious issues and fulfill the regular functions of HSR, the traditional methods for railway engineers involve carrying out regular on-site inspections manually or by semi-automatic inspection vehicles, and conducting timely corresponding repairing approaches and maintenance, where these methods are time-consuming and dangerous. In recent years, machine learning methods have been widely applied to the intelligent and automatic detection of severe defects in HSR. Currently, one of the most serious problems is the lack of sufficient high-quality data for model training, resulting in low recognition accuracy in HSR defects. To solve this problem, this paper proposed an intelligent recognition of defects in concrete slabs of HSR based on a few-shot learning model, that is, an artificial intelligence model based on limited data size, which recognizes three service conditions of concrete slabs in HSR: cracks, track board gaps, and unbroken state. Lightweight few-shot learning models specifically designed for HSR detection were proposed. Experiments were conducted to compare the performances of different lightweight-designed models, including accuracy, parameter quantity, and testing time. Results showed that the optimum model can fast and satisfactorily recognize the defects in HSR with a very limited data size of 10 samples for each training category, with a satisfactory accuracy of 73.9% in the test dataset with 20 samples for each category, parameter amounts of 2.8 million, and a testing time of 2.2 s per image. This study provides a reference for the automatic recognition of defects in HSR by railway engineers with insufficient samples.
published_date 2024-03-15T16:24:38Z
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