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 minutes 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.