Train Ego-Path Detection on Railway Tracks Using End-to-End Deep Learning

TL;DR

This paper presents TEP-Net, an end-to-end deep learning framework for precise train ego-path detection on railway tracks, extending the RailSem19 dataset and outperforming state-of-the-art methods in accuracy and speed.

Contribution

The introduction of the TEP-Net model and ego-path annotations on RailSem19 dataset advances railway track detection by focusing on the train's immediate path with improved accuracy and robustness.

Findings

TEP-Net achieves 97.5% IoU on test set.

TEP-Net outperforms existing methods in speed and accuracy.

The model demonstrates robustness across diverse conditions.

Abstract

This paper introduces the task of "train ego-path detection", a refined approach to railway track detection designed for intelligent onboard vision systems. Whereas existing research lacks precision and often considers all tracks within the visual field uniformly, our proposed task specifically aims to identify the train's immediate path, or "ego-path", within potentially complex and dynamic railway environments. Building on this, we extend the RailSem19 dataset with ego-path annotations, facilitating further research in this direction. At the heart of our study lies TEP-Net, an end-to-end deep learning framework tailored for ego-path detection, featuring a configurable model architecture, a dynamic data augmentation strategy, and a domain-specific loss function. Leveraging a regression-based approach, TEP-Net outperforms SOTA: while addressing the track detection problem in a more nuanced way than previously, our model achieves 97.5% IoU on the test set and is faster than all existing methods. Further comparative analysis highlights the relevance of the conceptual choices behind TEP-Net, demonstrating its inherent propensity for robustness across diverse environmental conditions and operational dynamics. This work opens promising avenues for the development of intelligent driver assistance systems and autonomous train operations, paving the way toward safer and more efficient railway transportation.