Research on vibration pattern recognition based on phase-sensitive optical time domain reflectometry and voting fully convolution neural networks

Abstract

A method that combines phase-sensitive optical time domain reflectometry with deep learning to construct new voting fully convolution neural networks (VoteFCNs) is proposed. Compared to the traditional convolutional network, the VoteFCN can be input with data of random size and requires less parameters so that the training speed can be improved greatly. The recognition results can be more accurate and more reliable if we use classification voting count and average recognition rate as the criteria to judge network training quality. At last, the training and identification were conducted by simulating such several disturbance events: walking, raining, climbing fence, hammering the ground optical fibre and normal outdoor environments. The results show that the average test accuracy of this method is about 93.4%.