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English(EN) DroneShield-AI: A Multi-Modal Sensor Fusion Framework for Real-Time Autonomous Drone Threat Detection, Behavioral Intent Classification, and Swarm Intelligence in Contested Airspace

DroneShield-AI框架以96%的准确率检测无人机威胁

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了DroneShield-AI，这是一个旨在实时检测和分类自主无人机威胁的开放框架。该系统集成了多种传感器输入，包括射频信号、声学特征以及由YOLOv8处理的视觉数据。它包含了一个新颖的行为意图分类引擎和一个用于分析无人机集群智能的图神经网络模块。该框架在检测无人机威胁和预测其行为方面表现出高准确率，所有相关代码和模型均已公开。 AI

影响 增强了自主无人机系统的实时威胁检测能力，可能提高了空域安全性。

排序理由 该集群包含一篇详细介绍用于无人机威胁检测的新型AI框架的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Marius Bayizere ·

    DroneShield-AI: A Multi-Modal Sensor Fusion Framework for Real-Time Autonomous Drone Threat Detection, Behavioral Intent Classification, and Swarm Intelligence in Contested Airspace

    arXiv:2606.11687v1 Announce Type: cross Abstract: Unmanned Aerial Vehicle (UAV) threats have emerged as a defining security challenge of the 21st century. This paper presents DroneShield-AI, a unified open framework integrating six processing layers: RF signal classification, aco…

  2. arXiv cs.CV TIER_1 English(EN) · Marius Bayizere ·

    DroneShield-AI: A Multi-Modal Sensor Fusion Framework for Real-Time Autonomous Drone Threat Detection, Behavioral Intent Classification, and Swarm Intelligence in Contested Airspace

    Unmanned Aerial Vehicle (UAV) threats have emerged as a defining security challenge of the 21st century. This paper presents DroneShield-AI, a unified open framework integrating six processing layers: RF signal classification, acoustic motor-signature detection, YOLOv8-based visu…

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