Railway Intrusion Events Classification and Location Based on Deep Learning in Distributed Vibration Sensing

With the rapid development of the high-speed railway industry, the safety of railway operations is becoming increasingly important. As a symmetrical structure, traditional manual patrol and camera surveillance solutions on both sides of the railway require enormous manpower and material resources and are highly susceptible to weather and electromagnetic interference. In contrast, a distributed fiber optic vibration sensing system can be continuously monitored and is not affected by electromagnetic interference to false alarms. However, it is still a challenge to identify the type of intrusion event along the fiber optic cable. In this paper, a railway intrusion event classification and location scheme based on a distributed vibration sensing system was proposed. In order to improve the accuracy and reliability of the recognition, a 1 DSE-ResNeXt+SVM method was demonstrated. Squeeze-and-excitation blocks with attention mechanisms increased the classification ability by sifting through feature information without being influenced by non-critical information, while a support vector machine classifier can further improve the classification accuracy. The method achieved an accuracy of 96.0% for the identification of railway intrusion events with the field experiments. It illustrates that the proposed scheme can significantly improve the safety of railway operations and reduce the loss of personnel and property safety.