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

• URL: http://arxiv.org/abs/2601.03302v1
• Date: Tue, 06 Jan 2026 03:39:59 GMT
• 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,
• Abstract summary: CageDroneRF (CDRF) is a large-scale benchmark for Radio-Frequency (RF) drone detection and identification.<n>CDRF addresses the scarcity and limited diversity of existing RF datasets.<n>This dataset spans a wide range of contemporary drone models.
• Score: 10.23151778679351
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 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.

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