CageDroneRF: A Large-Scale RF Benchmark and Toolkit for Drone Perception

TL;DR

CageDroneRF (CDRF) is a comprehensive large-scale RF drone detection benchmark with real-world and synthetic data, designed to improve the development and evaluation of RF perception models for diverse drone types and conditions.

Contribution

We introduce CDRF, a novel RF drone detection dataset with systematic augmentation tools, enabling standardized benchmarking and fostering progress in RF-based drone perception.

Findings

Dataset includes diverse drone models and conditions.

Open-source tools facilitate data generation and evaluation.

Supports classification, detection, and open-set recognition.

Abstract

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 recomputation for detection. The dataset spans a wide range of contemporary drone models, many of which are unavailable in current public datasets, and diverse 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. It enables standardized benchmarking for classification, open-set recognition, and object detection, supporting rigorous comparisons and reproducible pipelines. By releasing this comprehensive benchmark and tooling, we aim to accelerate progress toward robust, generalizable RF perception models.