Intrusion event recognition method for distributed fibre optic sensing systems based on Siamese networks

Due to its high sensitivity and wide measurement range, the distributed optical fibre sensing system has been widely used in long-distance infrastructure monitoring, where it detects vibration signals caused by external events and provides effective early warnings. Given the complexity and diversity of external intrusion events, traditional closed-set classification methods cannot effectively exclude unknown signals. Open-set recognition methods, on the other hand, involve complex model designs, requiring boundary Algorithms and often the inclusion of information related to unknown classes. In this paper, we combine a Siamese network with a residual network to recognise events in the distributed optical fibre sensing system. Through experiments, by comparing the similarity with data in the database, we not only ensure very high accuracy in closed-set classification but also effectively exclude unknown class signals. Experimental results show that our method successfully rejects unknown class data with a 92% success rate, while maintaining closed-set classification accuracy at an average of 99%. Our approach demonstrates excellent performance.