Dual-model hybrid pattern recognition method based on a fiber optic line-based sensor with a large amount of data

Abstract

A dual-model hybrid pattern recognition based on a fiber optic line-based sensor with a large amount of data is proposed. The vibration signals are converted to gray-level images to reduce the memory requirement. The ResNet18 model for classification is used. To reduce the false positive rate, the over-zero rate and short-time energy are extracted from the intrusion signal, and a support vector machine (SVM) is used. Finally, a discriminator is constructed to determine the types of events by combining the two models trained on the validation dataset. The results demonstrate the excellent average recognition accuracy of this method, which achieves the 97.1% for six events.