Integrating Object Detection, LiDAR-Enhanced Depth Estimation, and Segmentation Models for Railway Environments

TL;DR

This paper presents a modular framework combining object detection, track segmentation, and depth estimation with LiDAR data for railway obstacle detection, evaluated on synthetic data with promising accuracy.

Contribution

It introduces a flexible, integrated neural network system for obstacle detection and distance estimation in railway environments, addressing evaluation challenges with a synthetic dataset.

Findings

Achieved a mean absolute error of 0.63 meters in distance estimation.

Successfully integrated monocular depth maps with LiDAR data for improved accuracy.

Provided a synthetic dataset (SynDRA) for reliable evaluation.

Abstract

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.