We present CageDroneRF (CDRF), a large-scale benchmark for Radio-Frequency (RF) drone detection and identification built from real-world captures and systematically generated synthetic variants. CDRF addresses the scarcity and limited diversity of existing RF datasets by coupling extensive raw recordings with a principled augmentation pipeline that (i) precisely controls Signal-to-Noise Ratio (SNR), (ii) injects interfering emitters, and (iii) applies frequency shifts with label-consistent bounding-box transformations for detection. This dataset spans a wide range of contemporary drone models, many unavailable in current public datasets, and acquisition conditions, derived from data collected at the Rowan University campus and within a controlled RF-cage facility. CDRF is released with interoperable open-source tools for data generation, preprocessing, augmentation, and evaluation that also operate on existing public benchmarks. CDRF enables standardized benchmarking for classification, open-set recognition, and object detection, supporting rigorous comparisons and reproducible pipelines. By releasing this comprehensive benchmark and tooling, CDRF aims to accelerate progress toward robust, generalizable RF perception models.

翻译：我们提出了CageDroneRF（CDRF），一个用于射频（RF）无人机检测与识别的大规模基准数据集，该数据集基于真实世界采集数据并辅以系统生成的合成变体构建而成。CDRF通过将大量原始记录与一个原则性的数据增强流程相结合，解决了现有RF数据集稀缺且多样性有限的问题。该增强流程能够：（i）精确控制信噪比（SNR），（ii）注入干扰发射源，以及（iii）应用频移并配合标签一致的边界框变换以支持检测任务。该数据集涵盖了广泛的当代无人机型号（其中许多在当前公开数据集中不可得）以及多种采集条件，数据来源于罗文大学校园及一个受控的射频屏蔽室设施内进行的采集。CDRF随附一套可互操作的开源工具一同发布，这些工具可用于数据生成、预处理、增强和评估，并且同样适用于现有的公开基准。CDRF支持分类、开放集识别和目标检测任务的标准化基准测试，有助于进行严谨的比较和实现可复现的流程。通过发布这一全面的基准和工具，CDRF旨在加速鲁棒且可泛化的RF感知模型的研发进程。