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.

中文翻译：

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使用深度学习进行平交路口的物体检测

为了解决人口过剩的问题，铁路行业在不同地区开展了多个项目。这些扩展计划和技术升级增加了交叉路口，交叉路口和平交路口的数量。平交道口是指在不使用隧道或桥梁的情况下，铁路线在水平线上与道路或通行权交叉的地方。平交道口仍然对公众构成重大风险，这通常会导致铁路，公路和人行道使用者之间发生严重事故，并且风险取决于他们的不可预测行为。对于英国来说，2015-2016年的平交道口有3例死亡和385起未命中。此外，在年度安全报告中，铁路安全与标准委员会（RSSB）强调了2016/17年度在平交道口发生事故的风险，在平交道口还有六人死亡，包括四名行人和两辆公路车辆。有关当局建议升级现有的传感系统，并在平交道口整合新的新技术。本工作解决了这个关键问题，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。给定的信息足以使手动操作员做出决定或启动自动操作周期。传统传感器具有一定的局限性，通常被安装为“单个传感器”。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，该方法将背景像素与新像素进行比较。这种方法在动态和复杂的环境中无效。所提出的模型将深度学习技术与当前的视觉系统集成在一起（例如，通过CCTV在平交路口检测和定位对象）。提议的传感系统应该能够检测并定位特定对象（例如，平交区域的行人，自行车和车辆。）还讨论了雷达系统的“二分之三”逻辑联锁系统，以防发生故障。机制。讨论了用于训练深度学习模型的不同技术以及它们各自的结果。该模型比MobileNet模型的分类精度高约88％，损耗度量为0。092用于物体检测。还讨论了一些相关的未来工作。