Practical multi-class event classification approach for distributed vibration sensing using deep dual path network

Abstract

Influenced by severe ambient noises and nonstationary disturbance signals, multi-class event classification is an enormous challenge in several long-haul application fields of distributed vibration sensing technology (DVS), including perimeter security, railway safety monitoring, pipeline surveillance, etc. In this paper, a deep dual path network is introduced into solving this problem with high learning capacity. The spatial time-frequency spectrum datasets are built by utilizing the multidimensional information of DVS signal, especially the spatial domain information. With the novel datasets and a high-parameter-efficiency network, the proposed scheme presents good reliability and robustness. The feasibility is verified in an actual railway safety monitoring field test, as a proof-of-concept. Seven types of real-life disturbances were implemented and their f1-scores all reached up to 97% in the test. The performance of this proposed approach is fully evaluated and discussed. The presented approach can be employed to improve the performance of DVS in actual applications.