A 96pJ/Frame/Pixel and 61pJ/Event Anti-UAV System with Hybrid Object Tracking Modes

TL;DR

This paper introduces an energy-efficient anti-UAV system combining frame-based and event-driven tracking, achieving high accuracy and low power consumption through innovative hardware and adaptive algorithms.

Contribution

It presents a novel hybrid object tracking system with a custom neural processor and adaptive modes, significantly reducing energy use while maintaining high detection accuracy.

Findings

Achieves 96 pJ/frame/pixel and 61 pJ/event energy efficiency.

Reaches 98.2% recognition accuracy on UAV datasets.

Demonstrates state-of-the-art energy efficiency in anti-UAV applications.

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.