Lightweight fiber optic intrusion detection system

Abstract

Border security stands as a fundamental element in ensuring the property safety of citizens. Despite significant strides in artificial intelligence technology for border security and intrusion practices, the majority of research on intrusion signal classification primarily focuses on enhancing accuracy, with limited consideration of resource constraints. To construct a compact and efficient model that meets the requirements of small computing devices, this paper proposes a boundary security intrusion event recognition algorithm based on a distributed fiber optic sensing system. This algorithm combines the Gram Angle Field (GAF) and knowledge distillation network. The proposed approach establishes an efficient model with fewer parameters and computational resources. Compared to traditional vibration sensing systems, the Distributed Acoustic Sensing System (DAS) better leverages the advantages of distributed fiber extension. It utilizes the GAF to transform one-dimensional temporal signals into two-dimensional images, effectively filtering the impact of power fluctuations in the optical path on intrusion signals, and extracting deeper temporal features into the image. Knowledge distillation transfers the feature information trained by the teacher model to the smaller student model for intrusion signal recognition. The recognition accuracy reaches up to 98%, with a detection response time of approximately 0.78 s. Research results indicate that this approach can be utilized in developing high-precision lightweight intrusion signal detection models.