Φ-OTDR Event Recognition System Based on Valuable Data Selection

In the processing of recognizing phi-OTDR event signals by deep learning models, a large amount of annotated data is required for model training. However, the data annotation is a major challenge for a lot of unlabeled data and the labeling cost are high. This article proposes a valuable signal data selection method for phi-OTDR sensing system. The data samples that are beneficial for training from the unlabeled data set is selected based on their uncertainty values. Through only labeling these valuable data samples and training the classification model with them, the labeling cost can be saved a lot and the classification model can keep its classification accuracy. The experiments show that this method can save 83.77% on manual labeling cost and the classification accuracy can still reach 96.37%, which is similar to the classification accuracy of the model trained by labeling all the data.