Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasińskiego 8, 40-019 Katowice, Poland.
Sensors (Basel). 2021 Sep 18;21(18):6281. doi: 10.3390/s21186281.
The detection of obstacles at rail level crossings (RLC) is an important task for ensuring the safety of train traffic. Traffic control systems require reliable sensors for determining the state of anRLC. Fusion of information from a number of sensors located at the site increases the capability for reacting to dangerous situations. One such source is video from monitoring cameras. This paper presents a method for processing video data, using deep learning, for the determination of the state of the area (region of interest-ROI) vital for a safe passage of the train. The proposed approach is validated using video surveillance material from a number of RLC sites in Poland. The films include 24/7 observations in all weather conditions and in all seasons of the year. Results show that the recall values reach 0.98 using significantly reduced processing resources. The solution can be used as an auxiliary source of signals for train control systems, together with other sensor data, and the fused dataset can meet railway safety standards.
铁轨平交道口（RLC）障碍物的检测对于确保火车交通的安全至关重要。交通控制系统需要可靠的传感器来确定 RLC 的状态。来自位于现场的多个传感器的信息融合提高了对危险情况的反应能力。其中一个来源是监控摄像机的视频。本文提出了一种使用深度学习处理视频数据的方法，用于确定对火车安全通行至关重要的区域（感兴趣区域-ROI）的状态。该方法使用来自波兰多个 RLC 站点的视频监控材料进行了验证。这些影片包括全天候 24/7 观测以及一年中的所有季节。结果表明，使用显著减少的处理资源，召回值达到 0.98。该解决方案可用作与其他传感器数据一起的火车控制系统的辅助信号源，融合数据集可以满足铁路安全标准。
