# LiDAR-Guided Monocular 3D Object Detection for Long-Range Railway Monitoring Source: [LiDAR-Guided Monocular 3D Object Detection for Long-Range Railway Monitoring]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:35:18.340Z Authors: Raul David Dominguez Sanchez, Xavier Diaz Ortiz, Xingcheng Zhou, Max Peter Ronecker, Michael Karner, Daniel Watzenig, Alois Knoll Published: 2025-04-25T09:33:52.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()