Open-Set Event Recognition System Using Self-Supervised Contrastive Learning in Distributed Fiber Acoustic Sensing

Traditional open set recognition (OSR) methods, due to their supervised learning nature, primarily focus on extracting features of known event classes, failing to effectively capture the distinguishing features of unknown event classes, which limits recognition performance. This paper proposes a self-supervised contrastive learning-based open-set recognition (SCOR) method for DAS perimeter security systems. This method leverages self-supervised contrastive learning to distinguish between known and unknown class samples, significantly improving the recognition rate for unknown classes by integrating a consistency training strategy and a dual-level threshold detection approach. Moreover, the incorporation of center loss enhances the feature clustering of known class samples, thereby boosting the classification accuracy. The algorithm’s hyperparameters are optimized efficiently using Bayesian optimization hyperband (BOHB). These designs yield a more compact and separable visual semantic space, allowing the detected unknown samples to be further refined into more accurate latent subclasses through clustering. Experimental results based on multiple DAS datasets show that the proposed method achieves an accuracy of 94.1-98.2 % for known events and 88.7%-92.3% for unknown events, with an average improvement of 2.7%-7.8% in recognition accuracy compared to the best-performing alternative methods. Furthermore, the algorithm successfully enables efficient online detection through a self-built DAS perimeter security system and upper-level software in a laboratory environment.