Abnormal events detection based on RP and inception network using distributed optical fiber perimeter system. (February 2021)

• Record Type: Journal Article
• Authors: Lyu, Chengang; Jiang, Jianying; Li, Baihua; Huo, Ziqiang; Yang, Jiachen
• Is Part Of: Optics and lasers in engineering. Volume 137(2021)
• Publication Date: 2021-02
• Subjects: Distributed optical fiber sensing -- Events detection -- Inception network -- Recurrent plot; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2020.106377
• Languages: English
• ISSNs: 0143-8166

Abstract

Highlights: A novel perimeter security abnormal events identification method based on Recurrent Plot algorithm and neural network analysis. The Recurrent Plot algorithm extracts the motion characteristics of intrusion signals, which improves the accuracy of event identification. The Recurrent Plot algorithm encoding the signals into two-dimensional images, combined with the Convolutional Neural Networks to improve the efficiency of identification. Abstract: For establishing an accurate and reliable distributed optical fiber perimeter security system, this paper proposes a novel abnormity detection solution to security using Recurrent Plot (RP) and deep learning technology. Take advantage of the temporal correlation of intrusion signals, we encode the sensing signals into two-dimensional images through the RP algorithm. The RP algorithm can extract the motion characteristics of the signal from the complex time series, and it is robust to instrument noise. These encoded image signatures can reveal the deeper temporal correlation of the intrusion signals' motion. After that, Inception network can adaptively extract the features of these images to complete the accurate identification of a series of noisy intrusion signals. We conducted experiments on three most frequent natural events and three representative man-made intrusion events, including heavy rain, light rain, wind blowing, treading, slapping, and impacting. The results show that the detection accuracy has reached 99.7%. This method can achieve 0.35 s real-time detection in the online detection of abnormal events while ensuring accuracy, providing a new intrusion pattern identification idea for perimeter security.