用神经形态芯片实现低功耗无人机实时感知，续航超一年。
Drone Detection Using a Low-Power Neuromorphic Virtual Tripwire
- 基于事件相机与脉冲神经网络构建全神经形态检测系统。
- 功耗仅为边缘GPU方案的千分之一，支持电池供电运行超一年。
- 可部署为虚拟警戒线，适合无电区域或战区等严苛环境。
小型无人机对军事人员和民用基础设施构成日益严重的威胁，早期自动化检测至关重要。本文开发了一种基于脉冲神经网络与神经形态相机（事件相机）的无人机检测系统，并部署于神经形态芯片上，实现全神经形态架构。多个检测单元可协同部署形成虚拟警戒线，实时识别无人机进入受控区域的时间与位置。实验表明，该神经形态方案的能耗比部署在边缘GPU的参考方案低数个数量级，可在电池供电下持续运行超过一年。研究还探讨了合成数据在训练中的应用，结果表明模型主要依赖无人机的外形特征，而非螺旋桨的时序动态特性。系统体积小、功耗低，适用于缺乏电力设施的冲突区域或偏远地点。
原文摘要 · Abstract (English)
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.
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