Drone Detection with Event Cameras

TL;DR

This paper reviews how event-based cameras offer a robust, low-latency solution for drone detection, overcoming limitations of traditional cameras in challenging conditions and enabling advanced UAV tracking and identification.

Contribution

It provides a comprehensive survey of current event-based drone detection methods, highlighting their advantages and potential for future counter-UAV systems.

Findings

Event cameras reduce motion blur and perform well in extreme lighting.

Sparse asynchronous output suppresses static backgrounds.

Event-based methods enable real-time tracking and identification.

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.