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

2026-04-16

AI Summary

This study addresses the lack of flexible, deployable systems for simultaneously performing obstacle detection, track recognition, and accurate distance estimation in railway environments, a challenge exacerbated by the scarcity of real-world ground-truth data. To overcome this limitation, the work proposes the first modular framework that integrates object detection, rail semantic segmentation, and LiDAR-enhanced monocular depth estimation through a tri-branch neural network architecture operating in concert. The authors introduce SynDRA, a synthetic dataset, to circumvent the absence of real annotated data. Experimental results demonstrate that the proposed system achieves a mean absolute distance error of 0.63 meters on SynDRA, significantly advancing spatial perception and ranging accuracy in railway scenarios.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• obstacle detection
• railway environments
• depth estimation
• ground truth data
• distance estimation

Innovation

• LiDAR-enhanced depth estimation
• modular perception framework
• railway obstacle detection
• monocular depth fusion
• SynDRA dataset