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.

通过引入 “列车自我路径检测” 任务，本论文为智能车载视觉系统设计了一种精确的铁路轨道检测方法，并通过添加自我路径注释扩展了 RailSem19 数据集。TEP-Net 是一种端到端的深度学习框架，具备可配置的模型架构、动态数据增强策略和领域特定的损失函数，通过回归方法解决了轨道检测问题，并在测试集上实现了 97.5% 的 IoU，同时比现有方法更快。进一步的比较分析突显了 TEP-Net 背后的概念选择的相关性，表明其在不同的环境条件和运行动态下具有鲁棒性。本研究为智能驾驶辅助系统和自动列车运行的发展开辟了有前景的途径，为实现更安全、高效的铁路运输铺平了道路。