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

2026-03-06

AI Summary

This work addresses the limitations of existing drone detection methods based on frame-based cameras, which suffer degraded performance under high-speed or low-light conditions, and event-based approaches that often rely on static assumptions ill-suited for mobile platforms. To overcome these challenges, the authors propose a real-time, 360° drone detection system tailored for moving carriers. The system employs a rotating event camera to achieve panoramic horizontal coverage, introduces a novel image-like event representation that obviates the need for explicit motion compensation, and integrates a lightweight spatiotemporal neural network for efficient azimuth estimation. Implemented on an embedded Jetson Orin NX platform, outdoor experiments demonstrate that the system achieves an average angular error below 2° in challenging environments, meeting practical surveillance requirements.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• drone detection
• event camera
• moving platform
• real-time surveillance
• spinning camera

Innovation

• event camera
• drone detection
• spinning sensor
• motion-robust representation
• real-time embedded system