EventRadar: Long-Range Visual UAV Discovery through Spatiotemporal Event Sensing

2026-06-09

AI Summary

This work addresses the challenge of effectively detecting illicit drones at long ranges (700–1500 m), where conventional spatial-feature-based methods fail due to the targets’ extremely weak signatures. To overcome this limitation, the authors propose a novel event-camera-based detection framework that exploits the spatiotemporal periodic signals induced by rotor motion. By integrating IMU-derived pose information to construct a scene memory for static-background suppression, the method introduces—for the first time—a phase-agnostic harmonic evidence recovery mechanism. Central to this approach are two key innovations: Scene-Anchored Geometry Evidence (SAGE) and Comb-guided Harmonic-Group ISTA (CHG), which jointly enable real-time detection with low computational overhead. Experimental results demonstrate exceptional performance, achieving 0.990 mAP₃ and 0.949 F1₃ with a remarkably low miss rate of 0.009, thereby confirming the system’s high sensitivity and practical feasibility.

Abstract

Unauthorized unmanned aerial vehicle (UAV) activity around airports, public venues, and other sensitive sites has made protected-airspace monitoring increasingly important. A practical sensing system must search a wide angular region, find small long-range targets, and return both bearing support and UAV-specific evidence before a restricted perimeter is breached. Existing UAV detection paths often rely on spatially organized evidence, such as body extent, silhouette, or track continuity. At long range, however, these cues become difficult to preserve and verify as the target footprint weakens and its image-plane support shrinks. EventRadar follows a complementary cue: propeller-induced temporal periodicity, which recent event-camera sensing studies have shown can reveal UAV-specific motion after appearance becomes weak. We extend this cue to kilometer-scale active sensing with an event-camera prototype. Scene-Anchored Geometry Evidence (SAGE) fuses scanning events with IMU pose to maintain a bearing-indexed scene memory, separating transient candidate support from persistent background clutter. Comb-guided Harmonic-Group Learned Iterative Shrinkage and Thresholding Algorithm (CHG) then treats each candidate as a weak high-rate timing signal and recovers phase-insensitive harmonic evidence with fixed compute. Compared with related event-camera baselines on 700-1500 m UAV event recordings, EventRadar achieves 0.990 mAP$_{.3}$ and 0.949 F1$_{.3}$, reduces FN$_{.3}$ to 0.009, and shows real-time feasibility in prototype profiling.

Problem

• UAV detection
• long-range sensing
• event-based vision
• spatiotemporal sensing
• unauthorized drones

Innovation

• event camera
• temporal periodicity
• harmonic detection
• long-range UAV sensing
• spatiotemporal sensing