High-quality data augmentation method based on multi-scale dense attention-enhanced CGAN for Φ-OTDR event recognition

Abstract

A data augmentation method based on multi-scale dense attention-enhanced (MDA) conditional generative adversarial network (CGAN) is proposed for generating high-quality samples, which aims to address the challenge of limited data acquisition in phase-sensitive optical time-domain reflectometry ($\Phi$-OTDR). The raw data from the $\Phi$-OTDR system can be transformed into visualized two-dimensional images to extract real disturbance data, with each disturbance type displaying distinguishable features in the generated images. The MDA U-Net is incorporated as the generator in the CGAN to produce samples for dataset expansion, where the dense convolutional block attention module is added to the skip connection to focus on the key information of events, and the single-layer output of U-Net is replaced with a multi-scale connection output for multi-layer feature fusion. The discriminator adopts a multi-scale convolution structure to enhance the discriminant ability. The multi-event recognition experiments for the fenced fiber optic demonstrate that the augmented dataset enables three testing classification models to achieve recognition accuracy above 97.93%.