Enhancing Railway Maintenance Safety Using Open-Source Computer Vision
As high-speed railways continue to be constructed, more maintenance work is needed to ensure smooth operation. However, this leads to frequent accidents involving maintenance workers at the tracks. Although the number of such accidents is decreasing, there is an increase in the number of casualties....
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/5575557 |
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| _version_ | 1832546907311833088 |
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| author | Donghee Shin Jangwon Jin Jooyoung Kim |
| author_facet | Donghee Shin Jangwon Jin Jooyoung Kim |
| author_sort | Donghee Shin |
| collection | DOAJ |
| description | As high-speed railways continue to be constructed, more maintenance work is needed to ensure smooth operation. However, this leads to frequent accidents involving maintenance workers at the tracks. Although the number of such accidents is decreasing, there is an increase in the number of casualties. When a maintenance worker is hit by a train, it invariably results in a fatality; this is a serious social issue. To address this problem, this study utilized the tunnel monitoring system installed on trains to prevent railway accidents. This was achieved by using a system that uses image data from the tunnel monitoring system to recognize railway signs and railway tracks and detect maintenance workers on the tracks. Images of railway signs, tracks, and maintenance workers on the tracks were recorded through image data. The Computer Vision OpenCV library was utilized to extract the image data. A recognition and detection algorithm for railway signs, tracks, and maintenance workers was constructed to improve the accuracy of the developed prevention system. |
| format | Article |
| id | doaj-art-d6a54ee05c504eefb776fc4afba4aef4 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-d6a54ee05c504eefb776fc4afba4aef42025-02-03T06:46:42ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/55755575575557Enhancing Railway Maintenance Safety Using Open-Source Computer VisionDonghee Shin0Jangwon Jin1Jooyoung Kim2Railroad Operation Company, NEO TRANS Co. Ltd., Seongnam-Si, Gyeonggi-do 13524, Republic of KoreaGraduate School of Transportation, Korea National University of Transportation, Uiwang-si, Gyeonggi-do 16106, Republic of KoreaGraduate School of Transportation, Korea National University of Transportation, Uiwang-si, Gyeonggi-do 16106, Republic of KoreaAs high-speed railways continue to be constructed, more maintenance work is needed to ensure smooth operation. However, this leads to frequent accidents involving maintenance workers at the tracks. Although the number of such accidents is decreasing, there is an increase in the number of casualties. When a maintenance worker is hit by a train, it invariably results in a fatality; this is a serious social issue. To address this problem, this study utilized the tunnel monitoring system installed on trains to prevent railway accidents. This was achieved by using a system that uses image data from the tunnel monitoring system to recognize railway signs and railway tracks and detect maintenance workers on the tracks. Images of railway signs, tracks, and maintenance workers on the tracks were recorded through image data. The Computer Vision OpenCV library was utilized to extract the image data. A recognition and detection algorithm for railway signs, tracks, and maintenance workers was constructed to improve the accuracy of the developed prevention system.http://dx.doi.org/10.1155/2021/5575557 |
| spellingShingle | Donghee Shin Jangwon Jin Jooyoung Kim Enhancing Railway Maintenance Safety Using Open-Source Computer Vision Journal of Advanced Transportation |
| title | Enhancing Railway Maintenance Safety Using Open-Source Computer Vision |
| title_full | Enhancing Railway Maintenance Safety Using Open-Source Computer Vision |
| title_fullStr | Enhancing Railway Maintenance Safety Using Open-Source Computer Vision |
| title_full_unstemmed | Enhancing Railway Maintenance Safety Using Open-Source Computer Vision |
| title_short | Enhancing Railway Maintenance Safety Using Open-Source Computer Vision |
| title_sort | enhancing railway maintenance safety using open source computer vision |
| url | http://dx.doi.org/10.1155/2021/5575557 |
| work_keys_str_mv | AT dongheeshin enhancingrailwaymaintenancesafetyusingopensourcecomputervision AT jangwonjin enhancingrailwaymaintenancesafetyusingopensourcecomputervision AT jooyoungkim enhancingrailwaymaintenancesafetyusingopensourcecomputervision |