In response to the evolving challenges posed by small unmanned aerial vehicles (UAVs), which possess the potential to transport harmful payloads or independently cause damage, we introduce MMAUD: a comprehensive Multi-Modal Anti-UAV Dataset. MMAUD addresses a critical gap in contemporary threat detection methodologies by focusing on drone detection, UAV-type classification, and trajectory estimation. MMAUD stands out by combining diverse sensory inputs, including stereo vision, various Lidars, Radars, and audio arrays. It offers a unique overhead aerial detection vital for addressing real-world scenarios with higher fidelity than datasets captured on specific vantage points using thermal and RGB. Additionally, MMAUD provides accurate Leica-generated ground truth data, enhancing credibility and enabling confident refinement of algorithms and models, which has never been seen in other datasets. Most existing works do not disclose their datasets, making MMAUD an invaluable resource for developing accurate and efficient solutions. Our proposed modalities are cost-effective and highly adaptable, allowing users to experiment and implement new UAV threat detection tools. Our dataset closely simulates real-world scenarios by incorporating ambient heavy machinery sounds. This approach enhances the dataset's applicability, capturing the exact challenges faced during proximate vehicular operations. It is expected that MMAUD can play a pivotal role in advancing UAV threat detection, classification, trajectory estimation capabilities, and beyond. Our dataset, codes, and designs will be available in https://github.com/ntu-aris/MMAUD.

翻译：针对小型无人机（UAV）可能携带有害载荷或独立造成损害这一持续演变的挑战，我们提出MMAUD：一个综合性的多模态反无人机数据集。MMAUD通过聚焦于无人机检测、无人机类型分类及轨迹估计，填补了当代威胁检测方法中的关键空白。该数据集融合了立体视觉、多种激光雷达、雷达和音频阵列等多样化传感输入，具有独特性。它提供了独特的高空俯视检测视角，相较于在特定观测点使用热成像和RGB相机采集的数据集，能以更高保真度应对真实场景。此外，MMAUD提供了徕卡设备生成的高精度地面真值数据，增强了可信度，使得算法和模型的优化更具可靠性——这一特性在其他数据集中从未出现。鉴于现有研究大多未公开其数据集，MMAUD成为开发精确高效解决方案的宝贵资源。我们提出的模态方案成本低廉且高度适配，使用户能够实验并部署新型无人机威胁检测工具。该数据集通过融入环境重型机械噪声，紧密模拟真实场景。这一方法增强了数据集的适用性，精准捕捉了近距车辆作业时面临的真实挑战。预期MMAUD将在推动无人机威胁检测、分类、轨迹估计能力及其他相关领域发展中发挥关键作用。我们的数据集、代码及设计方案将在https://github.com/ntu-aris/MMAUD中公开。