Disturbance recognition for F-OTDR based on Faster-RCNN

Xu，Wei Jie1; Liu，Shuaiqi1,2; Yu，Fei Hong1; Shao，Liyang1

10.1117/12.2624215

2022

0277-786X

1996-756X

Proceedings of SPIE - The International Society for Optical Engineering

12169

This paper proposes a disturbance recognition method for phase-sensitive optical time-domain reflectometry (F-OTDR) based on Faster-RCNN. The method achieves high-speed detection of intrusion location and classification with high accuracy. Our scheme makes full use of the 2D sensing information on spatial-temporal images and uses the advanced "two-step" object detection algorithm Faster-RCNN to achieve real-time operation. Firstly, to improve the detection speed, Region Proposal Network (RPN) and Region of Interest (RoI) are used. Secondly, our CNN-based approach can extract features automatically of disturbance events from spatial-temporal images. So, it has better robustness compared to traditional machine learning methods. Thirdly, the method uses an end-to-end CNN object detection model that integrates multiple modules into a single network. Therefore, it has a significant advantage in detection speed. We conducted data collection under perimeter security scenarios and acquired 4 types of events with a total of 4987 samples. The four events contain “rigid collision”, “hitting net”, “shaking net”, and “cutting net”, which are representative in the perimeter security scenario. Experimental results proves that our method can achieve a real-time operation (0.1659 s processing time for 0.5 s sensing data) with high accuracy (96.32%), shows great potential in real-time disturbance detection for online monitoring industrial application of F-OTDR.

distributed fiber sensing disturbance recognition F-OTDR Faster-RCNN multi-class classification

第一 ; 通讯

英语

EI

Joint Fund of Research utilizing Large-scale Scientific Facilities[LZC0019];Australian Communications Consumer Action Network[LZC0020];

20221611967751

Image segmentation ; Intrusion detection ; Learning systems ; Object detection ; Object recognition ; Optical data processing ; Time domain analysis

Digital Computers and Systems:722.4 ; Computer Software, Data Handling and Applications:723 ; Data Processing and Image Processing:723.2 ; Mathematics:921

2-s2.0-85128026516

Scopus

1.Department of Electrical and Electronic Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.State Key Laboratory of Analog and Mixed-Signal VLSI,University of Macau,999078,Macao

Xu，Wei Jie,Liu，Shuaiqi,Yu，Fei Hong,et al. Disturbance recognition for F-OTDR based on Faster-RCNN[C],2022.