The proliferation of drones, or unmanned aerial vehicles (UAVs), has raised significant safety concerns due to their potential misuse in activities such as espionage, smuggling, and infrastructure disruption. This paper addresses the critical need for effective drone detection and classification systems that operate independently of UAV cooperation. We evaluate various convolutional neural networks (CNNs) for their ability to detect and classify drones using spectrogram data derived from consecutive Fourier transforms of signal components. The focus is on model robustness in low signal-to-noise ratio (SNR) environments, which is critical for real-world applications. A comprehensive dataset is provided to support future model development. In addition, we demonstrate a low-cost drone detection system using a standard computer, software-defined radio (SDR) and antenna, validated through real-world field testing. On our development dataset, all models consistently achieved an average balanced classification accuracy of >= 85% at SNR > -12dB. In the field test, these models achieved an average balance accuracy of > 80%, depending on transmitter distance and antenna direction. Our contributions include: a publicly available dataset for model development, a comparative analysis of CNN for drone detection under low SNR conditions, and the deployment and field evaluation of a practical, low-cost detection system.

翻译：随着无人机（UAV）的普及，其在间谍活动、走私和基础设施干扰等方面的潜在滥用引发了严重的安全担忧。本文针对不依赖无人机协作的有效检测与分类系统的迫切需求展开研究。我们评估了多种卷积神经网络（CNN）利用信号分量连续傅里叶变换生成的频谱图数据进行无人机检测与分类的能力。研究重点在于模型在低信噪比（SNR）环境下的鲁棒性，这对实际应用至关重要。本文提供了一个综合性数据集以支持未来模型开发。此外，我们展示了一种使用标准计算机、软件定义无线电（SDR）和天线的低成本无人机检测系统，并通过真实场景的实地测试进行了验证。在我们的开发数据集上，所有模型在SNR > -12dB时均持续取得了平均平衡分类准确率≥85%的成绩。在实地测试中，根据发射器距离和天线方向的不同，这些模型实现了平均平衡准确率>80%的性能。我们的贡献包括：一个公开可用的模型开发数据集、低SNR条件下CNN用于无人机检测的比较分析，以及一个实用低成本检测系统的部署与实地评估。