融合视觉与激光雷达，实现铁路场景中障碍物检测与精准测距。
Integrating Object Detection, LiDAR-Enhanced Depth Estimation, and Segmentation Models for Railway Environments
- 分模块集成目标检测、轨道分割与单目深度估计模型。
- 在合成数据集上实现0.63米均方误差的测距精度。
- 适合自动驾驶与铁路安全系统研发人员参考。
铁路环境中的障碍物检测对安全保障至关重要。然而，现有研究大多仅关注物体检测或轨道识别，极少同时实现障碍物定位与距离估计。此外，缺乏真实标注数据使得系统评估困难。本文提出一种模块化、灵活的框架，通过集成目标检测、轨道分割和结合激光雷达点云的单目深度估计三个神经网络，实现轨道识别、障碍物检测与距离估算。为支持可靠量化评估，采用合成数据集SynDRA进行测试，该数据集提供精确的真值标注。实验表明，融合单目深度图与激光雷达数据后，系统达到最低0.63米的均方误差，不仅提升了距离估计精度，还增强了对场景的空间感知能力。
原文摘要 · Abstract (English)
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.
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