Abstract
The rapid proliferation of drones requires balancing innovation with regulation. To address security and privacy concerns, techniques for drone detection have attracted significant this http URL 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 this http URL 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 this http URL, 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 this http URL 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° 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° under challenging conditions, underscoring its suitability for real-world surveillance applications. We will open-source our complete pipeline to support future research.
Abstract (translated)
无人机的迅速普及要求在创新与监管之间取得平衡。为了应对安全和隐私方面的担忧，无人机检测技术引起了广泛关注。目前的一些解决方案，如基于帧相机的系统，在常规条件下提供灵活性和能源效率，但从根本上受到操作原理的限制，在涉及快速移动目标或不利环境条件的情况下效果不佳。相比之下，生物视觉驱动的事件相机异步地检测像素亮度的变化，提供了高动态范围和微秒级响应速度，使它们特别适合在传统帧基系统难以发挥作用的条件下进行无人机检测。 然而，现有基于事件的大多数解决方案的设计假设摄像机是静态的，极大地限制了其在移动载体（如四足机器人或无人驾驶地面车辆）上的现场应用。在这篇论文中，我们介绍了一种为部署在移动载体上而设计的实时无人机检测系统。该系统使用旋转式的事件相机，提供360°水平视场，并能够估算被检无人机的方向。一个重要贡献是一种新颖的类似图像的事件表示方法，它无需运动补偿即可工作，并结合了一个轻量级神经网络架构以进行高效的时空学习。在车载Jetson Orin NX上实施后，该系统可以在实时环境中运行。 户外实验结果验证了其在挑战性条件下的可靠检测能力，平均角度误差低于2°，表明其适用于现实世界的监控应用。我们将开源我们的完整流程以支持未来的研究。
URL
https://arxiv.org/abs/2603.06265
