Fayyaz Muhammad Asad Bilal, Johnson Christopher
Department of Engineering, Manchester Metropolitan University, Manchester M15 6BH, UK.
Micromachines (Basel). 2020 Nov 29;11(12):1055. doi: 10.3390/mi11121055.
Multiple projects within the rail industry across different regions have been initiated to address the issue of over-population. These expansion plans and upgrade of technologies increases the number of intersections, junctions, and level crossings. A level crossing is where a railway line is crossed by a road or right of way on the level without the use of a tunnel or bridge. Level crossings still pose a significant risk to the public, which often leads to serious accidents between rail, road, and footpath users and the risk is dependent on their unpredictable behavior. For Great Britain, there were three fatalities and 385 near misses at level crossings in 2015-2016. Furthermore, in its annual safety report, the Rail Safety and Standards Board (RSSB) highlighted the risk of incidents at level crossings during 2016/17 with a further six fatalities at level crossings including four pedestrians and two road vehicles. The relevant authorities have suggested an upgrade of the existing sensing system and the integration of new novel technology at level crossings. The present work addresses this key issue and discusses the current sensing systems along with the relevant algorithms used for post-processing the information. The given information is adequate for a manual operator to make a decision or start an automated operational cycle. Traditional sensors have certain limitations and are often installed as a "single sensor". The single sensor does not provide sufficient information; hence another sensor is required. The algorithms integrated with these sensing systems rely on the traditional approach, where background pixels are compared with new pixels. Such an approach is not effective in a dynamic and complex environment. The proposed model integrates deep learning technology with the current Vision system (e.g., CCTV to detect and localize an object at a level crossing). The proposed sensing system should be able to detect and localize particular objects (e.g., pedestrians, bicycles, and vehicles at level crossing areas.) The radar system is also discussed for a "two out of two" logic interlocking system in case of fail-mechanism. Different techniques to train a deep learning model are discussed along with their respective results. The model achieved an accuracy of about 88% from the MobileNet model for classification and a loss metric of 0.092 for object detection. Some related future work is also discussed.
铁路行业在不同地区启动了多个项目，以解决人口过多的问题。这些扩张计划和技术升级增加了交叉路口、枢纽站和道口的数量。道口是指铁路线路与道路或通行权在同一平面交叉，且不使用隧道或桥梁的地方。道口仍然对公众构成重大风险，这常常导致铁路、道路和人行道使用者之间发生严重事故，而且风险取决于他们不可预测的行为。在英国，2015 - 2016年道口发生了3起死亡事故和385起险些发生的事故。此外，铁路安全标准委员会（RSSB）在其年度安全报告中强调了2016/17年度道口事故的风险，其中道口又有6人死亡，包括4名行人及2辆道路车辆。相关当局建议升级现有的传感系统，并在道口整合新的新技术。本工作解决了这一关键问题，并讨论了当前的传感系统以及用于信息后处理的相关算法。给定的信息足以让人工操作员做出决策或启动自动操作循环。传统传感器有一定的局限性，通常作为“单个传感器”安装。单个传感器无法提供足够的信息，因此需要另一个传感器。与这些传感系统集成的算法依赖于传统方法，即将背景像素与新像素进行比较。这种方法在动态复杂环境中并不有效。所提出的模型将深度学习技术与当前的视觉系统（如闭路电视，用于在道口检测和定位物体）相结合。所提出的传感系统应能够检测和定位特定物体（如道口区域的行人、自行车和车辆）。还讨论了用于故障机制情况下“二取二”逻辑联锁系统的雷达系统。讨论了训练深度学习模型的不同技术及其各自的结果。该模型在分类方面从MobileNet模型获得了约88%的准确率，在目标检测方面损失指标为0.092。还讨论了一些相关的未来工作。
