High-accuracy classification method of vibration sensing events in φ-OTDR system based on Vision Transformer

The distributed optical fiber sensing system based on φ-OTDR has a wide range of applications in the field of long-distance and large-scale monitoring due to its corrosion resistance, and strong anti-electromagnetic interference ability. Aiming at the problem of low accuracy and high false alarm rate of vibration sensing event classification in φ-OTDR system, a high-accuracy classification method based on Vision Transformer is proposed to identify and classify different types of vibration events, including background noise, digging, knocking, watering, shaking, and walking. Firstly, the two-dimensional space-time signal samples are divided into non-overlapping patches, these patches are flattened into a one-dimensional feature vector, and learnable classification tokens and position coding are embedded to form a feature vector sequence. Then, the global feature extraction and coding of the input feature vector sequence are performed through the Multi-Head Self-Attention mechanism of the Transformer, the backbone network. Finally, the output of the encoder is directly input into the multi-layer perceptron to output the classification results. The proposed method is compared with other three classification approaches based on deep learning, including 2DCNN, 1DCNN-BiLSTM, and an improved 1DCNN-BiLSTM known as 2DCNN-BiLSTM. The experimental results show that the average classification accuracy of the proposed method can reach 99.3%, the false alarm rate ranges from 0.2% to 1.8%, and all the precision, NAR and F1-score indicators are optimal.