[2601.03302] CageDroneRF: A Large-Scale RF Benchmark and Toolkit for Drone Perception

Computer Science > Computer Vision and Pattern Recognition arXiv:2601.03302 (cs) [Submitted on 6 Jan 2026 (v1), last revised 19 Mar 2026 (this version, v2)] Title:CageDroneRF: A Large-Scale RF Benchmark and Toolkit for Drone Perception Authors:Mohammad Rostami, Atik Faysal, Hongtao Xia, Hadi Kasasbeh, Ziang Gao, Huaxia Wang View a PDF of the paper titled CageDroneRF: A Large-Scale RF Benchmark and Toolkit for Drone Perception, by Mohammad Rostami and 5 other authors View PDF HTML (experimental) 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 ben...