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Real-Time Φ-OTDR Vibration Event Recognition Based on Image Target Detection
Accurate and fast identification of vibration signals detected based on the phase-sensitive optical time-domain reflectometer (Φ-OTDR) is crucial in reducing the false-alarm rate of the long-distance distributed vibration warning system. This study proposes a computer vision-based Φ-OTDR multi-vibra...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8840762/ https://www.ncbi.nlm.nih.gov/pubmed/35161872 http://dx.doi.org/10.3390/s22031127 |
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author | Yang, Nachuan Zhao, Yongjun Chen, Jinyang |
author_facet | Yang, Nachuan Zhao, Yongjun Chen, Jinyang |
author_sort | Yang, Nachuan |
collection | PubMed |
description | Accurate and fast identification of vibration signals detected based on the phase-sensitive optical time-domain reflectometer (Φ-OTDR) is crucial in reducing the false-alarm rate of the long-distance distributed vibration warning system. This study proposes a computer vision-based Φ-OTDR multi-vibration events detection method in real-time, which can effectively detect perimeter intrusion events and reduce personnel patrol costs. Pulse accumulation, pulse cancellers, median filter, and pseudo-color processing are employed for vibration signal feature enhancement to generate vibration spatio-temporal images and form a customized dataset. This dataset is used to train and evaluate an improved YOLO-A30 based on the YOLO target detection meta-architecture to improve system performance. Experiments show that using this method to process 8069 vibration data images generated from 5 abnormal vibration activities for two types of fiber optic laying scenarios, buried underground or hung on razor barbed wire at the perimeter of high-speed rail, the system mAP@.5 is 99.5%, 555 frames per second (FPS), and can detect a theoretical maximum distance of 135.1 km per second. It can quickly and effectively identify abnormal vibration activities, reduce the false-alarm rate of the system for long-distance multi-vibration along high-speed rail lines, and significantly reduce the computational cost while maintaining accuracy. |
format | Online Article Text |
id | pubmed-8840762 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88407622022-02-13 Real-Time Φ-OTDR Vibration Event Recognition Based on Image Target Detection Yang, Nachuan Zhao, Yongjun Chen, Jinyang Sensors (Basel) Article Accurate and fast identification of vibration signals detected based on the phase-sensitive optical time-domain reflectometer (Φ-OTDR) is crucial in reducing the false-alarm rate of the long-distance distributed vibration warning system. This study proposes a computer vision-based Φ-OTDR multi-vibration events detection method in real-time, which can effectively detect perimeter intrusion events and reduce personnel patrol costs. Pulse accumulation, pulse cancellers, median filter, and pseudo-color processing are employed for vibration signal feature enhancement to generate vibration spatio-temporal images and form a customized dataset. This dataset is used to train and evaluate an improved YOLO-A30 based on the YOLO target detection meta-architecture to improve system performance. Experiments show that using this method to process 8069 vibration data images generated from 5 abnormal vibration activities for two types of fiber optic laying scenarios, buried underground or hung on razor barbed wire at the perimeter of high-speed rail, the system mAP@.5 is 99.5%, 555 frames per second (FPS), and can detect a theoretical maximum distance of 135.1 km per second. It can quickly and effectively identify abnormal vibration activities, reduce the false-alarm rate of the system for long-distance multi-vibration along high-speed rail lines, and significantly reduce the computational cost while maintaining accuracy. MDPI 2022-02-02 /pmc/articles/PMC8840762/ /pubmed/35161872 http://dx.doi.org/10.3390/s22031127 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yang, Nachuan Zhao, Yongjun Chen, Jinyang Real-Time Φ-OTDR Vibration Event Recognition Based on Image Target Detection |
title | Real-Time Φ-OTDR Vibration Event Recognition Based on Image Target Detection |
title_full | Real-Time Φ-OTDR Vibration Event Recognition Based on Image Target Detection |
title_fullStr | Real-Time Φ-OTDR Vibration Event Recognition Based on Image Target Detection |
title_full_unstemmed | Real-Time Φ-OTDR Vibration Event Recognition Based on Image Target Detection |
title_short | Real-Time Φ-OTDR Vibration Event Recognition Based on Image Target Detection |
title_sort | real-time φ-otdr vibration event recognition based on image target detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8840762/ https://www.ncbi.nlm.nih.gov/pubmed/35161872 http://dx.doi.org/10.3390/s22031127 |
work_keys_str_mv | AT yangnachuan realtimephotdrvibrationeventrecognitionbasedonimagetargetdetection AT zhaoyongjun realtimephotdrvibrationeventrecognitionbasedonimagetargetdetection AT chenjinyang realtimephotdrvibrationeventrecognitionbasedonimagetargetdetection |