Real-Time Multi-Class Disturbance Detection for Φ-OTDR Based on YOLO Algorithm

Xu，Weijie1; Yu，Feihong1; Liu，Shuaiqi1,2; Xiao，Dongrui1; Hu，Jie1; Zhao，Fang1; Lin，Weihao1,2; Wang，Guoqing3; Shen，Xingliang1,4; Wang，Weizhi5; Wang，Feng6; Liu，Huanhuan1; Shum，Perry Ping1; Shao，Liyang1,5

2022-03-01

10.3390/s22051994

SENSORS   影响因子和分区

1424-8220

1424-8220

This paper proposes a real-time multi-class disturbance detection algorithm based on YOLO for distributed fiber vibration sensing. The algorithm achieves real-time detection of event location and classification on external intrusions sensed by distributed optical fiber sensing system (DOFS) based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). We conducted data collection under perimeter security scenarios and acquired five types of events with a total of 5787 samples. The data is used as a spatial–temporal sensing image in the training of our proposed YOLO-based model (You Only Look Once-based method). Our scheme uses the Darknet53 network to simplify the traditional two-step object detection into a one-step process, using one network structure for both event localization and classification, thus improving the detection speed to achieve real-time operation. Compared with the traditional Fast-RCNN (Fast Region-CNN) and Faster-RCNN (Faster Region-CNN) algorithms, our scheme can achieve 22.83 frames per second (FPS) while maintaining high accuracy (96.14%), which is 44.90 times faster than Fast-RCNN and 3.79 times faster than Faster-RCNN. It achieves real-time operation for locating and classifying intrusion events with continuously recorded sensing data. Experimental results have demonstrated that this scheme provides a solution to real-time, multi-class external intrusion events detection and classification for the Φ-OTDR-based DOFS in practical applications.

Distributed fiber sensing Multi-class classification Object detection Real-time detection YOLO Φ-OTDR

SCI ; EI

英语

第一 ; 通讯

Future Greater-Bay Area Network Facilities for Large-scale Experiments and Applications[LZC0019] ; Guangdong Department of Science and Technology[2021A0505080002] ; Shenzhen Science, Technology & Innovation Commission[20200925162216001] ; Guangdong Department of Education[2021ZDZX1023] ; Verification Platform of Multi-tier Coverage Communication Network for Oceans[LZC0020]

Chemistry ; Engineering ; Instruments & Instrumentation

Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation

WOS:000819938500001

MDPI

20221111782882

Intrusion detection ; Optical fibers ; Real time systems ; Signal detection

Information Theory and Signal Processing:716.1 ; Digital Computers and Systems:722.4 ; Computer Software, Data Handling and Applications:723 ; Data Processing and Image Processing:723.2 ; Fiber Optics:741.1.2

CHEMISTRY

2-s2.0-85126064807

Scopus

1.Department of Electrical and Electronic Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Department of Electrical and Computer Engineering,Faculty of Science and Technology,University of Macau,999078,Macao
3.Department of Microelectronics,Shenzhen Institute of Information Technology,Shenzhen,518172,China
4.The Department of Electronic and Information Engineering,Hong Kong Polytechnic University,Kowloon,Hong Kong
5.Peng Cheng Laboratory,Shenzhen,518005,China
6.College of Engineering and Applied Sciences,Nanjing University,Nanjing,210023,China

Xu，Weijie,Yu，Feihong,Liu，Shuaiqi,et al. Real-Time Multi-Class Disturbance Detection for Φ-OTDR Based on YOLO Algorithm[J]. SENSORS,2022,22(5).

Xu，Weijie.,Yu，Feihong.,Liu，Shuaiqi.,Xiao，Dongrui.,Hu，Jie.,...&Shao，Liyang.(2022).Real-Time Multi-Class Disturbance Detection for Φ-OTDR Based on YOLO Algorithm.SENSORS,22(5).

Xu，Weijie,et al."Real-Time Multi-Class Disturbance Detection for Φ-OTDR Based on YOLO Algorithm".SENSORS 22.5(2022).