LiDAR-Guided Monocular 3D Object Detection for Long-Range Railway Monitoring
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arXiv
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| Format: | Preprint |
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2025
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| author | Sanchez, Raul David Dominguez Ortiz, Xavier Diaz Zhou, Xingcheng Ronecker, Max Peter Karner, Michael Watzenig, Daniel Knoll, Alois |
| author_facet | Sanchez, Raul David Dominguez Ortiz, Xavier Diaz Zhou, Xingcheng Ronecker, Max Peter Karner, Michael Watzenig, Daniel Knoll, Alois |
| contents | Railway systems, particularly in Germany, require high levels of automation to address legacy infrastructure challenges and increase train traffic safely. A key component of automation is robust long-range perception, essential for early hazard detection, such as obstacles at level crossings or pedestrians on tracks. Unlike automotive systems with braking distances of ~70 meters, trains require perception ranges exceeding 1 km. This paper presents an deep-learning-based approach for long-range 3D object detection tailored for autonomous trains. The method relies solely on monocular images, inspired by the Faraway-Frustum approach, and incorporates LiDAR data during training to improve depth estimation. The proposed pipeline consists of four key modules: (1) a modified YOLOv9 for 2.5D object detection, (2) a depth estimation network, and (3-4) dedicated short- and long-range 3D detection heads. Evaluations on the OSDaR23 dataset demonstrate the effectiveness of the approach in detecting objects up to 250 meters. Results highlight its potential for railway automation and outline areas for future improvement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18203 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LiDAR-Guided Monocular 3D Object Detection for Long-Range Railway Monitoring Sanchez, Raul David Dominguez Ortiz, Xavier Diaz Zhou, Xingcheng Ronecker, Max Peter Karner, Michael Watzenig, Daniel Knoll, Alois Computer Vision and Pattern Recognition Machine Learning Railway systems, particularly in Germany, require high levels of automation to address legacy infrastructure challenges and increase train traffic safely. A key component of automation is robust long-range perception, essential for early hazard detection, such as obstacles at level crossings or pedestrians on tracks. Unlike automotive systems with braking distances of ~70 meters, trains require perception ranges exceeding 1 km. This paper presents an deep-learning-based approach for long-range 3D object detection tailored for autonomous trains. The method relies solely on monocular images, inspired by the Faraway-Frustum approach, and incorporates LiDAR data during training to improve depth estimation. The proposed pipeline consists of four key modules: (1) a modified YOLOv9 for 2.5D object detection, (2) a depth estimation network, and (3-4) dedicated short- and long-range 3D detection heads. Evaluations on the OSDaR23 dataset demonstrate the effectiveness of the approach in detecting objects up to 250 meters. Results highlight its potential for railway automation and outline areas for future improvement. |
| title | LiDAR-Guided Monocular 3D Object Detection for Long-Range Railway Monitoring |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2504.18203 |