Survey on vision-based railway track defect detection
The detection of train track defects is of great significance to the safety of rail transportation, but current manual inspection can no longer meet the complex and heavy track inspection requirements. Deep learning methods have greatly expanded the means and detection capabilities of defect detecti...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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POSTS&TELECOM PRESS Co., LTD
2024-09-01
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| Series: | 智能科学与技术学报 |
| Subjects: | |
| Online Access: | http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.202427 |
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| _version_ | 1846171096478384128 |
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| author | CHEN Tianyan HAN Zeming HUANG Yunhu SHI Jinjin CHEN Dewang |
| author_facet | CHEN Tianyan HAN Zeming HUANG Yunhu SHI Jinjin CHEN Dewang |
| author_sort | CHEN Tianyan |
| collection | DOAJ |
| description | The detection of train track defects is of great significance to the safety of rail transportation, but current manual inspection can no longer meet the complex and heavy track inspection requirements. Deep learning methods have greatly expanded the means and detection capabilities of defect detection, and in order to improve the efficiency and quality of surface defect detection, the current trends of visual detection methods in conjunction with the types and related attributes of track defects was systematically analyzed. The basic principles, technologies, methods, and current application status of visual detection methods for track and fastening defects, as well as the concepts, applications, and significance of these detection methods were elaborated. Finally, the current trends in the field of track defect detection are analyzed and summarized, and for the first time proposes the concept of a fully automatic track maintenance system, which aims to provide useful support and reference for future related research. |
| format | Article |
| id | doaj-art-193a11ab761043419a90bae3605ad5e6 |
| institution | Kabale University |
| issn | 2096-6652 |
| language | zho |
| publishDate | 2024-09-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 智能科学与技术学报 |
| spelling | doaj-art-193a11ab761043419a90bae3605ad5e62024-11-11T06:54:08ZzhoPOSTS&TELECOM PRESS Co., LTD智能科学与技术学报2096-66522024-09-01636738076168134Survey on vision-based railway track defect detectionCHEN TianyanHAN ZemingHUANG YunhuSHI JinjinCHEN DewangThe detection of train track defects is of great significance to the safety of rail transportation, but current manual inspection can no longer meet the complex and heavy track inspection requirements. Deep learning methods have greatly expanded the means and detection capabilities of defect detection, and in order to improve the efficiency and quality of surface defect detection, the current trends of visual detection methods in conjunction with the types and related attributes of track defects was systematically analyzed. The basic principles, technologies, methods, and current application status of visual detection methods for track and fastening defects, as well as the concepts, applications, and significance of these detection methods were elaborated. Finally, the current trends in the field of track defect detection are analyzed and summarized, and for the first time proposes the concept of a fully automatic track maintenance system, which aims to provide useful support and reference for future related research.http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.202427rail transportation safety;rail detect;image recognition;deep learning |
| spellingShingle | CHEN Tianyan HAN Zeming HUANG Yunhu SHI Jinjin CHEN Dewang Survey on vision-based railway track defect detection 智能科学与技术学报 rail transportation safety;rail detect;image recognition;deep learning |
| title | Survey on vision-based railway track defect detection |
| title_full | Survey on vision-based railway track defect detection |
| title_fullStr | Survey on vision-based railway track defect detection |
| title_full_unstemmed | Survey on vision-based railway track defect detection |
| title_short | Survey on vision-based railway track defect detection |
| title_sort | survey on vision based railway track defect detection |
| topic | rail transportation safety;rail detect;image recognition;deep learning |
| url | http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.202427 |
| work_keys_str_mv | AT chentianyan surveyonvisionbasedrailwaytrackdefectdetection AT hanzeming surveyonvisionbasedrailwaytrackdefectdetection AT huangyunhu surveyonvisionbasedrailwaytrackdefectdetection AT shijinjin surveyonvisionbasedrailwaytrackdefectdetection AT chendewang surveyonvisionbasedrailwaytrackdefectdetection |