Abstract
Potential safety hazards along the track primarily involve identifying lightweight objects within a 500-m radius on both sides. The existing manual inspection procedure is time-consuming and poses risks, particularly in remote regions. Unmanned aerial vehicles (UAV) offer a viable solution, efficiently conducting railway inspections with their wide field of view and flexibility. Image segmentation yields crucial, precise features essential for drone inspections. However, conventional techniques encounter challenges with drone imagery due to limited and non-diverse samples, hindering their ability to generalize. Additionally, these samples do not encompass all potential objects, necessitating frequent model updates—an arduous task. Accurately gauging the distance between the target and the railway track remains a challenge for precise risk assessment. To address these concerns, we introduce a method for segmenting potential hazards in the railway vicinity, utilizing the Fast Segment Anything Model (Fast SAM). This approach unfolds in three stages. First, data captured by drones undergoes image segmentation using the Fast SAM model, producing object masks. Subsequently, through various image processing techniques, we extract center points of hazards and endpoints of railway tracks from these segmented masks. This data facilitates distance computation between hazards and the railway using appropriate mathematical formulas. Lastly, hazard levels are determined based on predefined criteria. This comprehensive method augments hazard detection, inspection efficiency, and the accuracy of risk assessment within the railway vicinity.
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High-Speed Railway Safety Protection and Management Methods (2020) (in Chinese)
Wu, Y., Meng, F., Qin, Y., Qian, Y., Xu, F., Jia, L.: UAV imagery based potential safety hazard evaluation for high-speed railroad using Real-time instance segmentation. Adv. Eng. Inf. 55 (2023)
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Li, S., Yu, C., Chang, L., Zhao, X. (2024). Railway Surrounding Environment Hazard Detection Based on Fast SAM. In: Yang, J., Yao, D., Jia, L., Qin, Y., Liu, Z., Diao, L. (eds) Proceedings of the 6th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2023. EITRT 2023. Lecture Notes in Electrical Engineering, vol 1136. Springer, Singapore. https://doi.org/10.1007/978-981-99-9315-4_63
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DOI: https://doi.org/10.1007/978-981-99-9315-4_63
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