Effective zero-shot learning method for event classification in Φ-OTDR sensing systems

Despite various Φ-OTDR intrusion event recognition methods having achieved high average accuracy rates (over 90%), these methods usually rely on a large amount of training sample data (80% of the data). When faced with certain intrusion events that are difficult to simulate or have few samples available, the model tends to overfit common types of intrusion events. To address this issue, this paper proposes a zero-sample learning one-dimensional residual model based on attribute point loss (APL-ZSL-1DResNet) to recognize one-dimensional intrusion event signals when training samples are insufficient. The proposed method is validated on two datasets, including a self-made dataset and an open dataset. In the experiments, each category of samples was set as zero-sample intrusion events, achieving an average recall rate of 75% and 66% respectively for zero-sample events, and an average recall rate of 94.6% and 83.5% respectively for common intrusion events.