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http://hdl.handle.net/10397/96552
Title: | Real-time multi-class disturbance detection for φ-OTDR based on YOLO algorithm | Authors: | Xu, W Yu, F Liu, S Xiao, D Hu, J Zhao, F Lin, W Wang, G Shen, X Wang, W Wang, F Liu, H Shum, PP Shao, L |
Issue Date: | Mar-2022 | Source: | Sensors, Mar. 2022, v. 22, no. 5, 1994 | 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. | Keywords: | Distributed fiber sensing Multi-class classification Object detection Real-time detection YOLO Φ-OTDR |
Publisher: | Molecular Diversity Preservation International (MDPI) | Journal: | Sensors | EISSN: | 1424-8220 | DOI: | 10.3390/s22051994 | 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/). 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. |
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