This paper presents an on-board multi-sensor system which is able to detect obstacles and estimate their distances in railway scenes in different illumination conditions. The system was developed within the H2020 Shift2Rail project SMART (Smart Automation of Rail Transport) and aims at increasing the safety of rail transport by detecting obstacles on the rail tracks ahead of a moving train in order to reduce the number of collisions. The system hardware consists of cameras of different types integrated into a specially designed housing, mounted on the front of the train. Multiple vision sensors complement each other in order to handle different illumination and environmental conditions. The system software uses a novel machine learning-based method that is suited to a particular challenge of railway operations, the need for long-range obstacle detection and distance estimation. The development of this method used a long-range railway dataset, which was specifically generated for this project. Evaluation results of reliable obstacle detection in various environmental conditions using the SMART RGB camera in day light illumination conditions and using the SMART Night Vision sensor in poor (night) illumination conditions are presented. The results demonstrate both the potential of the on-board SMART obstacle detection system in the operational railway environment and the benefit of the use of different cameras to be more independent of light and environmental conditions.

本文提出了一种车载多传感器系统，该系统能够在不同光照条件下的铁路场景中检测障碍物并估计它们的距离。该系统是在 H2020 Shift2Rail 项目 SMART（轨道交通智能自动化）中开发的，旨在通过检测移动列车前方轨道上的障碍物来提高轨道交通的安全性，以减少碰撞次数。系统硬件由不同类型的摄像机组成，它们集成在一个专门设计的外壳中，安装在火车的前部。多个视觉传感器相互补充，以处理不同的照明和环境条件。系统软件使用一种新颖的基于机器学习的方法，适用于铁路运营的特定挑战，需要远程障碍物检测和距离估计。该方法的开发使用了专门为本项目生成的远程铁路数据集。介绍了在白天光照条件下使用 SMART RGB 摄像头和在较差（夜间）光照条件下使用 SMART 夜视传感器在各种环境条件下可靠障碍物检测的评估结果。结果证明了车载 SMART 障碍检测系统在铁路运营环境中的潜力，以及使用不同摄像头的好处，更不受光线和环境条件的影响。介绍了在白天光照条件下使用 SMART RGB 摄像头和在较差（夜间）光照条件下使用 SMART 夜视传感器在各种环境条件下可靠障碍物检测的评估结果。结果证明了车载 SMART 障碍检测系统在铁路运营环境中的潜力，以及使用不同摄像头的好处，更不受光线和环境条件的影响。介绍了在白天光照条件下使用 SMART RGB 摄像头和在较差（夜间）光照条件下使用 SMART 夜视传感器在各种环境条件下可靠障碍物检测的评估结果。结果证明了车载 SMART 障碍检测系统在铁路运营环境中的潜力，以及使用不同摄像头的好处，更不受光线和环境条件的影响。