An Easy Access Method for Event Recognition of Φ-OTDR Sensing System Based on Transfer Learning

Abstract

Traditional event recognition methods for signal collected by Φ-OTDR sensing system is difficult to identify the event category accurately in field application. Deep-learning-based event recognition method can achieve high classification accuracy but needs massive scale computation and long-term training. An event recognition method based on transfer training which can build a high-precision event recognition network quickly is proposed in this paper. The raw data collected by Φ-OTDR only needs simple bandpass filtering and scaling according to the size of the input layer of the pre-trained network. The initial network is created by freezing the front structure of the pre-trained network and only the rest layers are trained. The experiment result based on 4254 samples from a 8 kinds event data set showed that through freezing one-fifth of the pre-trained AlexNet, which is trained on the ImageNet data set, and retraining the rest parts by Nvidia GTX1050Ti, which contains only 768 CUDA cores, for less than 5\xa0minutes can achieve the best classification accuracy, which is about 96.16%. When the training data set reduces to only 1146 samples, the method can still achieve 95.56% classification accuracy. It provides a way to quickly build a high-accuracy network for a new filed application.