We present an energy-efficient anti-UAV system that integrates frame-based and event-driven object tracking to enable reliable detection of small and fast-moving drones. The system reconstructs binary event frames using run-length encoding, generates region proposals, and adaptively switches between frame mode and event mode based on object size and velocity. A Fast Object Tracking Unit improves robustness for high-speed targets through adaptive thresholding and trajectory-based classification. The neural processing unit supports both grayscale-patch and trajectory inference with a custom instruction set and a zero-skipping MAC architecture, reducing redundant neural computations by more than 97 percent. Implemented in 40 nm CMOS technology, the 2 mm^2 chip achieves 96 pJ per frame per pixel and 61 pJ per event at 0.8 V, and reaches 98.2 percent recognition accuracy on public UAV datasets across 50 to 400 m ranges and 5 to 80 pixels per second speeds. The results demonstrate state-of-the-art end-to-end energy efficiency for anti-UAV systems.

翻译：本文提出一种高能效的反无人机系统，该系统融合了基于帧和事件驱动的目标跟踪，以实现对小型快速移动无人机的可靠检测。该系统利用游程编码重建二值事件帧，生成候选区域，并根据目标大小与速度自适应地在帧模式与事件模式之间切换。一个快速目标跟踪单元通过自适应阈值和基于轨迹的分类，提升了对高速目标的跟踪鲁棒性。神经处理单元支持灰度图像块和轨迹推断，采用自定义指令集和零值跳过乘累加架构，将冗余的神经计算减少了97%以上。该芯片采用40纳米CMOS工艺实现，面积为2平方毫米，在0.8V电压下，实现了每帧每像素96皮焦耳和每个事件61皮焦耳的能量效率，并在公开的无人机数据集上，在50至400米距离范围和每秒5至80像素速度范围内，达到了98.2%的识别准确率。这些结果表明了该系统在反无人机领域具有先进的端到端能效。