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This paper introduces a modular framework for railway obstacle detection that integrates object detection, track segmentation, and LiDAR-enhanced monocular depth estimation. The system leverages three neural networks and fuses monocular depth with LiDAR point clouds to improve distance estimation. Evaluated on a new synthetic dataset (SynDRA) with ground truth annotations, the framework achieves a mean absolute error of 0.63 meters in distance estimation.
Accurate railway obstacle detection is now possible thanks to a modular framework that fuses LiDAR and monocular depth estimation, achieving sub-meter distance accuracy.
Obstacle detection in railway environments is crucial for ensuring safety. However, very few studies address the problem using a complete, modular, and flexible system that can both detect objects in the scene and estimate their distance from the vehicle. Most works focus solely on detection, others attempt to identify the track, and only a few estimate obstacle distances. Additionally, evaluating these systems is challenging due to the lack of ground truth data. In this paper, we propose a modular and flexible framework that identifies the rail track, detects potential obstacles, and estimates their distance by integrating three neural networks for object detection, track segmentation, and monocular depth estimation with LiDAR point clouds. To enable a reliable and quantitative evaluation, the proposed framework is assessed using a synthetic dataset (SynDRA), which provides accurate ground truth annotations, allowing for direct performance comparison with existing methods. The proposed system achieves a mean absolute error (MAE) as low as 0.63 meters by integrating monocular depth maps with LiDAR, enabling not only accurate distance estimates but also spatial perception of the scene.
