Deep learning based identification and tracking of railway bogie parts
The Train Rolling-Stock Examination (TRSE) is a safety examination process that physically examines the bogie parts of a moving train, typically at speeds over 30 km/h. Currently, this inspection process is done manually by railway personnel in many countries to ensure safety and prevent interruptio...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S111001682400797X |
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| _version_ | 1846167087423160320 |
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| author | Muhammad Zakir Shaikh Zeeshan Ahmed Enrique Nava Baro Samreen Hussain Mariofanna Milanova |
| author_facet | Muhammad Zakir Shaikh Zeeshan Ahmed Enrique Nava Baro Samreen Hussain Mariofanna Milanova |
| author_sort | Muhammad Zakir Shaikh |
| collection | DOAJ |
| description | The Train Rolling-Stock Examination (TRSE) is a safety examination process that physically examines the bogie parts of a moving train, typically at speeds over 30 km/h. Currently, this inspection process is done manually by railway personnel in many countries to ensure safety and prevent interruptions to rail services. Although many earlier attempts have been made to semi-automate this process through computer-vision models, these models are iterative and still require manual intervention. Consequently, these attempts were unsuitable for real-time implementations. In this work, we propose a detection model by utilizing a deep-learning based classifier that can precisely identify bogie parts in real-time without manual intervention, allowing an increase in the deployability of these inspection systems. We implemented the Anchor-Free Yolov8 (AFYv8) model, which has a decoupled-head module for recognizing bogie parts. Additionally, we incorporated bogie parts tracking with the AFYv8 model to gather information about any missing parts. To test the effectiveness of the AFYv8-model, the bogie videos were captured at three different timestamps and the result shows the increase in the recognition accuracy of TRSE by 10 % compared to the previously developed classifiers. This research has the potential to enhance railway safety and minimize operational interruptions. |
| format | Article |
| id | doaj-art-0129aa0264a5422a99160894b6098506 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-0129aa0264a5422a99160894b60985062024-11-15T06:11:14ZengElsevierAlexandria Engineering Journal1110-01682024-11-01107533546Deep learning based identification and tracking of railway bogie partsMuhammad Zakir Shaikh0Zeeshan Ahmed1Enrique Nava Baro2Samreen Hussain3Mariofanna Milanova4NCRAAI MUET & NCRA-CMS Lab, Mehran University of Engineering and Technology (MUET), Jamshoro 76062, Pakistan; Universidad de Malaga, Málaga, Spain; Correspondence to: NCRAAI MUET & NCRA-CMS Lab, Mehran University of Engineering and Technology (MUET), Jamshoro 76062, Pakistan and School of Industrial Engineering, University of Malaga, Spain.NCRAAI MUET & NCRA-CMS Lab, Mehran University of Engineering and Technology (MUET), Jamshoro 76062, PakistanUniversidad de Malaga, Málaga, SpainDawood University of Engineering & Technology, Karachi 74800, PakistanDepartment of Computer Science, University of Arkansas, Little Rock, USAThe Train Rolling-Stock Examination (TRSE) is a safety examination process that physically examines the bogie parts of a moving train, typically at speeds over 30 km/h. Currently, this inspection process is done manually by railway personnel in many countries to ensure safety and prevent interruptions to rail services. Although many earlier attempts have been made to semi-automate this process through computer-vision models, these models are iterative and still require manual intervention. Consequently, these attempts were unsuitable for real-time implementations. In this work, we propose a detection model by utilizing a deep-learning based classifier that can precisely identify bogie parts in real-time without manual intervention, allowing an increase in the deployability of these inspection systems. We implemented the Anchor-Free Yolov8 (AFYv8) model, which has a decoupled-head module for recognizing bogie parts. Additionally, we incorporated bogie parts tracking with the AFYv8 model to gather information about any missing parts. To test the effectiveness of the AFYv8-model, the bogie videos were captured at three different timestamps and the result shows the increase in the recognition accuracy of TRSE by 10 % compared to the previously developed classifiers. This research has the potential to enhance railway safety and minimize operational interruptions.http://www.sciencedirect.com/science/article/pii/S111001682400797XComputer VisionObject DetectionDeep LearningYoloTrain Rolling StockWheelset |
| spellingShingle | Muhammad Zakir Shaikh Zeeshan Ahmed Enrique Nava Baro Samreen Hussain Mariofanna Milanova Deep learning based identification and tracking of railway bogie parts Alexandria Engineering Journal Computer Vision Object Detection Deep Learning Yolo Train Rolling Stock Wheelset |
| title | Deep learning based identification and tracking of railway bogie parts |
| title_full | Deep learning based identification and tracking of railway bogie parts |
| title_fullStr | Deep learning based identification and tracking of railway bogie parts |
| title_full_unstemmed | Deep learning based identification and tracking of railway bogie parts |
| title_short | Deep learning based identification and tracking of railway bogie parts |
| title_sort | deep learning based identification and tracking of railway bogie parts |
| topic | Computer Vision Object Detection Deep Learning Yolo Train Rolling Stock Wheelset |
| url | http://www.sciencedirect.com/science/article/pii/S111001682400797X |
| work_keys_str_mv | AT muhammadzakirshaikh deeplearningbasedidentificationandtrackingofrailwaybogieparts AT zeeshanahmed deeplearningbasedidentificationandtrackingofrailwaybogieparts AT enriquenavabaro deeplearningbasedidentificationandtrackingofrailwaybogieparts AT samreenhussain deeplearningbasedidentificationandtrackingofrailwaybogieparts AT mariofannamilanova deeplearningbasedidentificationandtrackingofrailwaybogieparts |