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

arXiv: https://arxiv.org/abs/2601.03302 · HTML: https://mlsanity.com/paper/2601.03302/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 83 references that are in the map.
- First posted on arXiv 6 Jan 2026.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors](https://mlsanity.com/paper/2007.07396/) (2020)
- [Drone classification from RF fingerprints using deep residual nets](https://mlsanity.com/paper/2011.13663/) (2020)
- [Multimodal Object Detection using Depth and Image Data for Manufacturing Parts](https://mlsanity.com/paper/2411.09062/) (2024)
- [Machine Learning Framework for RF-Based Drone Detection and Identification System](https://mlsanity.com/paper/2003.02656/) (2020)
- [Wavelet Transform Analytics for RF-Based UAV Detection and Identification System Using Machine Learning](https://mlsanity.com/paper/2102.11894/) (2021)
- [Robust Low-Cost Drone Detection and Classification in Low SNR Environments](https://mlsanity.com/paper/2406.18624/) (2024)
- [MetaMax: Improved Open-Set Deep Neural Networks via Weibull Calibration](https://mlsanity.com/paper/2211.10872/) (2022)
- [RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification](https://mlsanity.com/paper/2503.09033/) (2025)
- [RF-Based Direction Finding of UAVs Using DNN](https://mlsanity.com/paper/1712.01154/) (2017)

## Abstract (arXiv)

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.