Using deep learning model integration to build a smart railway traffic safety monitoring system
Abstract According to the importance of railway safety, it is crucial to build a smart railway traffic safety system in Taiwan, especially there are often to see related accidents. Therefore, this study aimed to build a smart railway traffic safety system using the integration of object detection, s...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88830-7 |
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| author | Chin-Chieh Chang Kai-Hsiang Huang Tsz-Kin Lau Chung-Fah Huang Chun-Hsiung Wang |
| author_facet | Chin-Chieh Chang Kai-Hsiang Huang Tsz-Kin Lau Chung-Fah Huang Chun-Hsiung Wang |
| author_sort | Chin-Chieh Chang |
| collection | DOAJ |
| description | Abstract According to the importance of railway safety, it is crucial to build a smart railway traffic safety system in Taiwan, especially there are often to see related accidents. Therefore, this study aimed to build a smart railway traffic safety system using the integration of object detection, segmentation, machine learning, and notification system. First, the Mask R-CNN model was applied to automatically build the digital boundaries of railway, which achieved an average Interest of Union (IOU) of over 0.9. Then, the YOLO v3 model was applied to detect intrusions of railway, especially humans’ intrusion. The above object detection model achieved an Overall accuracy (OA) of over 90% for different classes, and an OA of 95.68% for human detection. The YOLO v3 model was also able to detect intrusion within different scenarios, such as nighttime, rainy daytime, and rainy nighttime. Moreover, the XGBoost model was applied to predict the sizes of intruding objects, which has a low MAE of 0.54 cm and an R2 score of 0.997. Finally, the LINE bot was applied to notify the related operators, including the above information, such as time of intrusion, locations, classes of intruding objects, sizes and the image of intrusion. The above implementation can be helpful for railway traffic safety monitoring, which may help related accidents prevention. |
| format | Article |
| id | doaj-art-6631d671618549df826c1052444b416b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-6631d671618549df826c1052444b416b2025-02-09T12:30:44ZengNature PortfolioScientific Reports2045-23222025-02-0115111310.1038/s41598-025-88830-7Using deep learning model integration to build a smart railway traffic safety monitoring systemChin-Chieh Chang0Kai-Hsiang Huang1Tsz-Kin Lau2Chung-Fah Huang3Chun-Hsiung Wang4National Kaohsiung University of Science and TechnologyNational Kaohsiung University of Science and TechnologyNational Kaohsiung University of Science and TechnologyNational Kaohsiung University of Science and TechnologyNational Kaohsiung University of Science and TechnologyAbstract According to the importance of railway safety, it is crucial to build a smart railway traffic safety system in Taiwan, especially there are often to see related accidents. Therefore, this study aimed to build a smart railway traffic safety system using the integration of object detection, segmentation, machine learning, and notification system. First, the Mask R-CNN model was applied to automatically build the digital boundaries of railway, which achieved an average Interest of Union (IOU) of over 0.9. Then, the YOLO v3 model was applied to detect intrusions of railway, especially humans’ intrusion. The above object detection model achieved an Overall accuracy (OA) of over 90% for different classes, and an OA of 95.68% for human detection. The YOLO v3 model was also able to detect intrusion within different scenarios, such as nighttime, rainy daytime, and rainy nighttime. Moreover, the XGBoost model was applied to predict the sizes of intruding objects, which has a low MAE of 0.54 cm and an R2 score of 0.997. Finally, the LINE bot was applied to notify the related operators, including the above information, such as time of intrusion, locations, classes of intruding objects, sizes and the image of intrusion. The above implementation can be helpful for railway traffic safety monitoring, which may help related accidents prevention.https://doi.org/10.1038/s41598-025-88830-7Railway traffic safetyObject detectionObject segmentationMachine learning |
| spellingShingle | Chin-Chieh Chang Kai-Hsiang Huang Tsz-Kin Lau Chung-Fah Huang Chun-Hsiung Wang Using deep learning model integration to build a smart railway traffic safety monitoring system Scientific Reports Railway traffic safety Object detection Object segmentation Machine learning |
| title | Using deep learning model integration to build a smart railway traffic safety monitoring system |
| title_full | Using deep learning model integration to build a smart railway traffic safety monitoring system |
| title_fullStr | Using deep learning model integration to build a smart railway traffic safety monitoring system |
| title_full_unstemmed | Using deep learning model integration to build a smart railway traffic safety monitoring system |
| title_short | Using deep learning model integration to build a smart railway traffic safety monitoring system |
| title_sort | using deep learning model integration to build a smart railway traffic safety monitoring system |
| topic | Railway traffic safety Object detection Object segmentation Machine learning |
| url | https://doi.org/10.1038/s41598-025-88830-7 |
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