LiDAR-Guided Monocular 3D Object Detection for Long-Range Railway Monitoring
Journal:
arXiv
Published Date:
Apr 25, 2025
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.
