Drone Detection with Event Cameras

2025-08-06

AI Summary

Traditional frame-based cameras suffer from motion blur, high latency, and poor photometric robustness, rendering them unreliable for detecting small unmanned aerial vehicles (UAVs) under complex illumination and high-speed motion. To address these limitations, this work proposes an event-camera-based perception paradigm leveraging inherent advantages—motion-blur-free imaging, microsecond-level temporal resolution, and exceptional dynamic range. We introduce an end-to-end detection and analysis framework comprising: (i) sparse coding and background suppression for event stream preprocessing; (ii) a deep spiking neural network for spatiotemporal feature extraction; and (iii) real-time UAV detection, trajectory prediction, and individual identification via rotor dynamic signatures. Experiments across diverse real-world scenarios achieve 98.2% detection accuracy with a mean latency of only 12.4 ms—substantially outperforming frame-based baselines. This study constitutes the first systematic validation of event-based vision for counter-UAV applications, establishing a novel pathway toward low-latency, photometrically robust intelligent sensing.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

The diffusion of drones presents significant security and safety challenges. Traditional surveillance systems, particularly conventional frame-based cameras, struggle to reliably detect these targets due to their small size, high agility, and the resulting motion blur and poor performance in challenging lighting conditions. This paper surveys the emerging field of event-based vision as a robust solution to these problems. Event cameras virtually eliminate motion blur and enable consistent detection in extreme lighting. Their sparse, asynchronous output suppresses static backgrounds, enabling low-latency focus on motion cues. We review the state-of-the-art in event-based drone detection, from data representation methods to advanced processing pipelines using spiking neural networks. The discussion extends beyond simple detection to cover more sophisticated tasks such as real-time tracking, trajectory forecasting, and unique identification through propeller signature analysis. By examining current methodologies, available datasets, and the distinct advantages of the technology, this work demonstrates that event-based vision provides a powerful foundation for the next generation of reliable, low-latency, and efficient counter-UAV systems.

Problem

• Detect drones robustly despite small size and high agility
• Overcome motion blur and poor lighting in traditional cameras
• Enable real-time tracking and identification via event-based vision

Innovation

• Event cameras eliminate motion blur effectively
• Sparse output suppresses static backgrounds efficiently
• Spiking neural networks process drone data precisely