Phase-sensitive Optical Time Domain Reflectometry (Φ-OTDR) is easy to be interfered by ambient noises, and the nonlinear coherent addition of different interferences always makes it difficult to detect real human intrusions and causes high Nuisance Alarm Rates (NARs) in practical applications. In this paper, an effective temporal signal separation and determination method is proposed to improve its detection performance in complicated noisy environments. Unlike the conventional analysis of transverse spatial signals, the time-evolving sensing signal of Φ-OTDR system is at first obtained for each spatial point by accumulating the changing OTDR traces at different moments. Then its longitudinal temporal signal is decomposed and analyzed by a multi-scale wavelet decomposition method. By selectively recombining the corresponding scale components, it can effectively extract human intrusion signals, and separate the influences of slow change of the system and other environmental interferences. Compared with the conventional differentiation way and FFT denoising method, the Signal-to-Noise-Ratios (SNRs) of the detecting signals for the proposed method is always the best, which can be raised by up to ~35dB for the best case. Moreover, from the decomposed components, different event signals can be effectively determined by their energy distribution features, and the NAR can be controlled to be less than 2% in the test.
Existing detection methods face a huge challenge in identifying insulators with minor defects when targeting transmission line images with complex backgrounds. To ensure the safe operation of transmission lines, an improved YOLOv7 model is proposed to improve detection results. Firstly, the target boxes of the insulator dataset are clustered based on K-means++ to generate more suitable anchor boxes for detecting insulator-defect targets. Secondly, the Coordinate Attention (CoordAtt) module and HorBlock module are added to the network. Then, in the channel and spatial domains, the network can enhance the effective features of the feature-extraction process and weaken the ineffective features. Finally, the SCYLLA-IoU (SIoU) and focal loss functions are used to accelerate the convergence of the model and solve the imbalance of positive and negative samples. Furthermore, to optimize the overall performance of the model, the method of non-maximum suppression (NMS) is improved to reduce accidental deletion and false detection of defect targets. The experimental results show that the mean average precision of our model is 93.8%, higher than the Faster R-CNN model, the YOLOv7 model, and YOLOv5s model by 7.6%, 3.7%, and 4%, respectively. The proposed YOLOv7 model can effectively realize the accurate detection of small objects in complex backgrounds.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.