Abstract
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.
Abstract (translated)
我们提出了一种节能的反无人机系统，该系统结合了基于帧和事件驱动的对象跟踪技术，以实现对小型快速移动无人机的可靠检测。该系统使用行程长度编码重构二进制事件帧，并生成区域提案。根据目标大小和速度，它可以自适应地在帧模式和事件模式之间切换。快速物体跟踪单元通过自适应阈值调整和基于轨迹分类提高了高速目标的鲁棒性。神经处理单元支持灰度补丁和轨迹推理，采用了一套定制指令集以及零跳过MAC（矩阵乘法累加）架构，在减少冗余神经计算方面超过了97%的效率。 在40纳米CMOS技术中实现的芯片面积为2平方毫米，该系统在0.8伏电压下每帧每像素的能量消耗为96皮焦耳，每个事件的能量消耗为61皮焦耳。它在公开的无人机数据集上实现了高达98.2%的识别准确率，在50到400米的距离范围内和每秒5至80个像素的速度时性能均表现良好。 实验结果表明该系统达到了反无人机系统的端到端能耗效率的新水平。
URL
https://arxiv.org/abs/2512.17939
