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

arXiv: https://arxiv.org/abs/2604.14781 · HTML: https://mlsanity.com/paper/2604.14781/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 16 Apr 2026.

## Builds on (most cited first, 13 of 13)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Unsupervised Monocular Depth Estimation with Left-Right Consistency](https://mlsanity.com/paper/1609.03677/) (2016)
- [OSDaR23: Open Sensor Data for Rail 2023](https://mlsanity.com/paper/2305.03001/) (2023)
- [Absolute distance prediction based on deep learning object detection and monocular depth estimation models](https://mlsanity.com/paper/2111.01715/) (2021)
- [TrainSim: A Railway Simulation Framework for LiDAR and Camera Dataset Generation](https://mlsanity.com/paper/2302.14486/) (2023)
- [Towards Railway Domain Adaptation for LiDAR-based 3D Detection: Road-to-Rail and Sim-to-Real via SynDRA-BBox](https://mlsanity.com/paper/2507.16413/) (2025)
- [OSDaR-AR: Enhancing Railway Perception Datasets via Multi-modal Augmented Reality](https://mlsanity.com/paper/2602.22920/) (2026)
- [Object Depth and Size Estimation using Stereo-vision and Integration with SLAM](https://mlsanity.com/paper/2409.07623/) (2024)

## Abstract (arXiv)

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.