ODD-SEC: Onboard Drone Detection with a Spinning Event Camera

TL;DR

This paper presents a real-time drone detection system using a spinning event camera on moving carriers, leveraging a novel event representation and neural network for high-accuracy outdoor detection.

Contribution

It introduces a novel event-based drone detection method with a spinning camera and a new image-like event representation that works without motion compensation.

Findings

Reliable outdoor detection with mean angular error below 2°

Operates in real time on embedded hardware

Effective in challenging lighting and motion conditions

Abstract

The rapid proliferation of drones requires balancing innovation with regulation. To address security and privacy concerns, techniques for drone detection have attracted significant attention.Passive solutions, such as frame camera-based systems, offer versatility and energy efficiency under typical conditions but are fundamentally constrained by their operational principles in scenarios involving fast-moving targets or adverse illumination.Inspired by biological vision, event cameras asynchronously detect per-pixel brightness changes, offering high dynamic range and microsecond-level responsiveness that make them uniquely suited for drone detection in conditions beyond the reach of conventional frame-based cameras.However, the design of most existing event-based solutions assumes a static camera, greatly limiting their applicability to moving carriers--such as quadrupedal robots or unmanned ground vehicles--during field operations.In this paper, we introduce a real-time drone detection system designed for deployment on moving carriers. The system utilizes a spinning event-based camera, providing a 360{\deg} horizontal field of view and enabling bearing estimation of detected drones. A key contribution is a novel image-like event representation that operates without motion compensation, coupled with a lightweight neural network architecture for efficient spatiotemporal learning. Implemented on an onboard Jetson Orin NX, the system can operate in real time. Outdoor experimental results validate reliable detection with a mean angular error below 2{\deg} under challenging conditions, underscoring its suitability for real-world surveillance applications. We will open-source our complete pipeline to support future research.