# Robust LiDAR-Based Train Detection via Point Cloud Segmentation for Railway Safety

## TL;DR

This paper introduces a new LiDAR-based method for detecting trains using point cloud segmentation, improving railway safety monitoring in challenging conditions.

## Contribution

The paper proposes a lightweight 3D segmentation network for direct train point prediction, enhancing detection accuracy and robustness.

## Key findings

- The proposed method outperforms traditional background comparison techniques in detection accuracy.
- It shows greater robustness to changes in LiDAR installation and environmental conditions.
- The approach is suitable for real-time train detection in railway safety monitoring.

## Abstract

Ensuring railway safety requires reliable monitoring of trains in critical safety areas, such as station throat zones and railway crossings. Compared with cameras, roadside LiDAR can more reliably capture the geometry of trains under low-light, high-speed, and adverse weather conditions. However, industrial LiDAR solutions still primarily use the background comparison technique, which compares each sample against a pre-recorded clean map and then applies a size-based filter. Such approaches are highly sensitive to point cloud background changes arising from varying LiDAR installation distances, train speeds, and surface materials, often resulting in fragmented clustering and missed detections. In this paper, train detection is reformulated as a point-level semantic segmentation problem. A lightweight 3D segmentation network that directly predicts train points from raw data is designed, and clustering-based post-processing is applied to generate train-level events in real time. Experiments on real railway data under various operating conditions show that the proposed method achieves higher detection accuracy and greater robustness than traditional compare-based methods and representative deep learning benchmark methods, and is therefore suitable for practical railway safety monitoring.