Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/96552
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electronic and Information Engineering | - |
dc.creator | Xu, W | en_US |
dc.creator | Yu, F | en_US |
dc.creator | Liu, S | en_US |
dc.creator | Xiao, D | en_US |
dc.creator | Hu, J | en_US |
dc.creator | Zhao, F | en_US |
dc.creator | Lin, W | en_US |
dc.creator | Wang, G | en_US |
dc.creator | Shen, X | en_US |
dc.creator | Wang, W | en_US |
dc.creator | Wang, F | en_US |
dc.creator | Liu, H | en_US |
dc.creator | Shum, PP | en_US |
dc.creator | Shao, L | en_US |
dc.date.accessioned | 2022-12-07T02:55:24Z | - |
dc.date.available | 2022-12-07T02:55:24Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/96552 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.rights | © 2022 by the authors. 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/). | en_US |
dc.rights | The following publication Xu, W., Yu, F., Liu, S., Xiao, D., Hu, J., Zhao, F., ... & Shao, L. (2022). Real-Time Multi-Class Disturbance Detection for Φ-OTDR Based on YOLO Algorithm. Sensors, 22(5), 1994 is available at https://doi.org/10.3390/s22051994. | en_US |
dc.subject | Distributed fiber sensing | en_US |
dc.subject | Multi-class classification | en_US |
dc.subject | Object detection | en_US |
dc.subject | Real-time detection | en_US |
dc.subject | YOLO | en_US |
dc.subject | Φ-OTDR | en_US |
dc.title | Real-time multi-class disturbance detection for φ-OTDR based on YOLO algorithm | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 22 | en_US |
dc.identifier.issue | 5 | en_US |
dc.identifier.doi | 10.3390/s22051994 | en_US |
dcterms.abstract | 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. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Sensors, Mar. 2022, v. 22, no. 5, 1994 | en_US |
dcterms.isPartOf | Sensors | en_US |
dcterms.issued | 2022-03 | - |
dc.identifier.scopus | 2-s2.0-85126064807 | - |
dc.identifier.pmid | 35271143 | - |
dc.identifier.eissn | 1424-8220 | en_US |
dc.identifier.artn | 1994 | en_US |
dc.description.validate | 202212 bckw | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | - |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
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sensors-22-01994.pdf | 24.85 MB | Adobe PDF | View/Open |
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