EventRadar: Long-Range Visual UAV Discovery through Spatiotemporal Event Sensing
Quick Answer
EventRadar introduces a novel UAV detection system utilizing propeller-induced temporal periodicity for long-range monitoring, achieving 0.990 mAP and 0.949 F1 scores on 700-1500 m UAV recordings.
Quick Take
EventRadar introduces a novel UAV detection system utilizing propeller-induced temporal periodicity for long-range monitoring, achieving 0.990 mAP and 0.949 F1 scores on 700-1500 m UAV recordings. The system effectively reduces false negatives to 0.009, demonstrating real-time feasibility in prototype profiling.
Key Points
- EventRadar employs event-camera technology for kilometer-scale UAV detection.
- Utilizes Scene-Anchored Geometry Evidence (SAGE) to enhance detection accuracy.
- Achieves 0.990 mAP and 0.949 F1 scores, outperforming existing methods.
- Reduces false negatives to 0.009, improving reliability in monitoring.
- Demonstrates real-time processing capabilities in prototype evaluations.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 11285v1 Announce Type: new 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.
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