Multi-event recognition for φ-OTDR system based on CNN-SVM method

Yuan Ji; Jianming Shao; Shuanglong Li; Chen Ye

doi:10.1117/12.2668076

20 April 2023 Multi-event recognition for φ-OTDR system based on CNN-SVM method

Yuan Ji, Jianming Shao, Shuanglong Li, Chen Ye

Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 126020R (2023) https://doi.org/10.1117/12.2668076
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China

Abstract

Phase-sensitive optical time-domain reflectometry (Φ-OTDR) can monitor vibration signals in real time, but lack the ability to identify multiple events such as human activity and vehicle activity. With the help of machine learning methods, events can basically be identified. For the recognition of events such as human activities and vehicle activities, and to improve the classification accuracy, this paper adopts an event recognition method based on the combination of deep learning and traditional classifiers. The spatiotemporal data matrix from Φ-OTDR is directly sent to a convolutional neural network (CNN), and data features can be acquired automatically. The data features are sent to traditional classifiers for further classification. In this paper, support vector machine (SVM) is selected as the classifier to improve the classification accuracy. The experiment collected 4428 sets of data of four events. Experiments verifies the feasibility of multi-event recognition system of φ-OTDR based on CNN-SVM, and realized the recognition accuracy rate of four events was 68%- 81%.

Citation Download Citation

Yuan Ji, Jianming Shao, Shuanglong Li, and Chen Ye "Multi-event recognition for φ-OTDR system based on CNN-SVM method", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 126020R (20 April 2023); https://doi.org/10.1117/12.2668076

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