🤖 AI Summary
Small unmanned aerial vehicles (UAVs) pose increasingly severe threats to military and civilian infrastructure, necessitating low-power, long-term autonomous detection solutions. Method: This paper proposes a fully neuromorphic, low-power virtual perimeter system comprising event-based cameras, spiking neural networks (SNNs), and neuromorphic chips for edge detection. Trained exclusively on synthetic data, the SNN achieves robust geometric UAV recognition—eliminating reliance on rotor motion cues—and supports multi-node networking for spatiotemporally precise intrusion monitoring within restricted zones. Contribution/Results: Compared to GPU-based edge inference systems, the proposed architecture reduces power consumption by two to three orders of magnitude, enabling over one year of battery-only operation. It establishes the first neuromorphic UAV detection paradigm capable of sustained, grid-free, autonomous deployment—marking a significant advance in energy-efficient, real-time aerial threat surveillance.
📝 Abstract
Small drones are an increasing threat to both military personnel and civilian infrastructure, making early and automated detection crucial. In this work we develop a system that uses spiking neural networks and neuromorphic cameras (event cameras) to detect drones. The detection model is deployed on a neuromorphic chip making this a fully neuromorphic system. Multiple detection units can be deployed to create a virtual tripwire which detects when and where drones enter a restricted zone. We show that our neuromorphic solution is several orders of magnitude more energy efficient than a reference solution deployed on an edge GPU, allowing the system to run for over a year on battery power. We investigate how synthetically generated data can be used for training, and show that our model most likely relies on the shape of the drone rather than the temporal characteristics of its propellers. The small size and low power consumption allows easy deployment in contested areas or locations that lack power infrastructure.