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Railway systems, particularly in Germany, require high levels of automationto address legacy infrastructure challenges and increase train traffic safely.A key component of automation is robust long-range perception, essential forearly hazard detection, such as obstacles at level crossings or pedestrians ontracks. Unlike automotive systems with braking distances of ~70 meters, trainsrequire perception ranges exceeding 1 km. This paper presents andeep-learning-based approach for long-range 3D object detection tailored forautonomous trains. The method relies solely on monocular images, inspired bythe Faraway-Frustum approach, and incorporates LiDAR data during training toimprove depth estimation. The proposed pipeline consists of four key modules:(1) a modified YOLOv9 for 2.5D object detection, (2) a depth estimationnetwork, and (3-4) dedicated short- and long-range 3D detection heads.Evaluations on the OSDaR23 dataset demonstrate the effectiveness of theapproach in detecting objects up to 250 meters. Results highlight its potentialfor railway automation and outline areas for future improvement.

LiDAR-Guided Monocular 3D Object Detection for Long-Range Railway Monitoring