Autonomous Real-Time Regional Risk Monitoring for Unmanned Swarm Systems

Existing State-of-the-Art (SOTA) methods for situational awareness typically rely on high-bandwidth transmission of raw data or computationally intensive models, which are often impractical for resource-constrained edge devices in unstable communication environments. To address these limitations, this paper introduces a comprehensive framework for Regional Risk Monitoring utilizing unmanned swarm systems. We propose an innovative knowledge distillation approach (SIKD) that leverages both soft label dark knowledge and inter-layer relationships, enabling compressed models to run in real time on edge nodes while maintaining high accuracy. Furthermore, recognition results are fused using Bayesian inference to dynamically update the regional risk level. Experimental results demonstrate the feasibility of the proposed framework. Quantitatively, the proposed SIKD algorithm reduces the model parameters by 52.34% and computational complexity to 44.21% of the original model, achieving a 3× inference speedup on edge CPUs. Furthermore, it outperforms state-of-the-art baseline methods (e.g., DKD and IRG) in terms of convergence speed and classification accuracy, ensuring robust real-time risk monitoring.