Distributed acoustic sensing for fence monitoring: deep learning approach for detection and classification of events on various types of fence

Abstract

One of the most prominent applications of fiber optic Distributed Acoustic Sensing (DAS) is Perimeter Security via fence monitoring, which is possible when we attach a fiber to the fence. In this study, we aim to detect and classify events occurring around said fence, such as climbing, cutting, and bending. For this, we investigate Deep Learning algorithms, more specifically Convolutional Neural Networks (CNN), as a mean to detect anomalies and classify them. We recorded 48,445 samples of the mentioned events, which were carefully processed and labeled. From each record, we exploited multiple data instances, resulting in a large enough training dataset to produce a robust classifier. We report the optimum network architecture that suited our study for both the anomaly detection and classification task. The optimal model is tested before and after deployment on-site, we report the quantified performance on a test set via a confusion matrix, and observations about the model’s behaviour on the field. Furthermore, we compare our trials and results on two types of fences, namely rigid and loose, to show how it affects the performance of the trained CNN models, as the signal propagates differently between rigid and loose clotures. We report an overall accuracy of 96.15% for the optimal anomaly detection model, and a lower 52.9% for the 3-class classification model. These results are explained and commented on. Finally, we conclude by providing an educated proposal for future improvements.