Open-Set Events Identification Based on Deep Metric-Learning for DMZI Perimeter System

Intrusion monitoring based on optical fiber distributed sensing is a pattern recognition problem of target events. Due to the addition of uncontrollable unknown events in practical applications, the open-set problem brought about greatly reduces the accuracy of traditional events recognition methods. In order to solve this problem, this paper design a deep metric-learning network, combined with Recurrent Plot (RP) coding to improve the accuracy of target events recognition in an open environment. In this paper, the RP algorithm is used to encode the intrusion signals into images that reflect the signal's motion. The deep metric-learning network is used to project the image into the feature space. And feature centers of each class can be calculated rely on the training samples. So, the network identifies the intrusion event according to the distance between the test sample and the feature center. By setting the appropriate threshold, the samples whose distance exceeds the threshold are identified as unknown class to solve the open-set problem. In the experiments, the dual Mach-Zehnder interferometry (DMZI) distributed optical fiber perimeter system is built to collect event signals. For 7 known event classes the highest identification accuracy can reach 99.7%. After adding 3 unknown event classes, the accuracy rate can exceed 93.3% with appropriate threshold. And the total response time is only 0.4 s. This novel method guarantees the accuracy of target identification under the introduction of unknown events and improves the robustness of the distributed optical fiber perimeter security system.