The distributed optical fiber vibration sensing (DOFVS) system is a pre-alarm system based on security monitoring technology, which can realize continuous distributed detection and measurement of vibration events along single optical fiber links. The DOFVS system has many advantages such as high positioning accuracy, a large monitoring range, simple structure, and easy installation, and it has been widely and successfully used in many vibration sensing fields, such as long-distance oil and gas pipeline leak detection, security monitoring of transmission line networks, and perimeter security monitoring. However, due to the complexity and diversity of its application environment, the DOFVS system still faces problems such as low reliability and poor stability in practical applications. In our research, we propose an intelligent sensing detection scheme, which combines the DOFVS system and artificial intelligence (AI). This scheme can significantly improve the practical reliability and stability of the DOFVS system in engineering applications.
This paper proposes an accurate detection scheme for multiple optical fiber vibration sensing events based on the You Only Look Once version 5s (YOLOv5s) model by integrating the dual Mach-Zehnder interferometer (DMZI) system and the quadrotor unmanned aerial vehicle (UAV) monitoring system. When an intrusion event occurs, the DMZI system transmits the location of the disturbance point to the UAV via Qgroundcontrol. After the UAV flies to the disturbance point, the camera on it can automatically capture and photograph the surrounding environment of the vibration position in real time and then transmit the real-time image information back to the ground station through the first-person view (FPV). First, the DMZI system and the UAV system are controlled by the ground station Qgroundcontrol. Second, the short-time Fourier transform (STFT) is performed to obtain the corresponding two-dimensional spectrum from the one-dimensional time-series signal. Third, the spectrum of the two-dimensional vibration signal and the corresponding original images captured by the UAV are jointly sent into the YOLOv5s-based convolutional neural network (CNN) model for identification and classification. Fourth, massive experiments are carried out to verify the effectiveness and feasibility of the proposed scheme. The mean average precision (mAP) and identification times of the five sensing events are measured to demonstrate the performance of the proposed scheme.
According to the application requirements, this paper proposes and designs a vibration identification scheme based on the DMZI-UAV-fused security system, which is realized by the combination of STFT and the YOLOv5s algorithm. By the DMZI-UAV-based combination security monitoring system, the features of the optical path signal from the perspective of time and frequency can be effectively extracted. Moreover, the proposed scheme can also discriminate and classify the intrusion events in the actual space with high efficiency. The method based on the YOLOv5s algorithm can automatically extract features, which avoids the low robustness problem in manual feature extraction. The effectiveness of the method is verified by the detection of five common sensing events, namely, no intrusion, waggling, knocking, crashing, and fence kicking. The training results show that the mAP for the five sensing events is all above 95%. Furthermore, the field test results demonstrate that the proposed scheme can accurately identify and classify five typical sensing events, with mAP of 96.6%. Meanwhile, compared with traditional machine learning and other deep learning schemes, the proposed scheme has a significantly shorter response time that can be controlled within 5 ms. Therefore, we believe that the proposed scheme can improve the reliability and stability of the DMZI DOFVS system in practical engineering applications.