Intrusion Detection for High-speed Railway System: A Faster R-CNN Approach

Recently, the abnormal intrusion detection has become an urgent problem in high-speed railway system. One way to solve this problem is the optical fiber distributed acoustic sensing (DAS) system that can monitor the intrusion events and provide early warning. However, most long-distance DAS systems are unable to distinguish signal types to improve the detection performance. Moreover, the traditional fiber optic sensing system is susceptible to interference from environmental factors, resulting in false detections and alarms. To this end, with the adoption of DAS system, we propose a railway intrusion detection system based on Faster R-CNN. In our system, we first design the DAS system to collect the optical fiber acoustic signals. Then, the collected signals are normalized in temporal and spatial dimensions and converted into Spatio-temporal images. After that, we design the Faster R-CNN algorithm to extract the Spatio-temporal features to detect and classify five types of abnormal intrusion events. The experimental results demonstrate that the average detection precision of our system for all abnormal intrusion events is above 89%. In addition, compared with the conventional methods, our system achieves the highest detection precision. Meanwhile, the system can distinguish the non-threatening background noise, which is of great help to reduce the system false positive rate.