kitoweeknd/RFUAV
A benchmark dataset and deep learning models for Radio-Frequency-based drone detection and identification using CNN, YOLO, and Transformer architectures on RF signal data.
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RFUAV provides a comprehensive benchmark dataset of Radio-Frequency recordings from 35 drone types for detection and identification tasks. The repository includes pre-trained deep learning models implementing a two-stage pipeline for drone signal detection and classification. It combines signal processing techniques (FFT/STFT) with modern neural architectures including CNNs, YOLO object detectors, and Transformers operating on raw IQ data and spectral representations.
Frequently asked
- What is kitoweeknd/RFUAV?
- A benchmark dataset and deep learning models for Radio-Frequency-based drone detection and identification using CNN, YOLO, and Transformer architectures on RF signal data.
- Is RFUAV open source?
- Yes — kitoweeknd/RFUAV is open source, released under the Apache-2.0 license.
- What language is RFUAV written in?
- kitoweeknd/RFUAV is primarily written in Python.
- How popular is RFUAV?
- kitoweeknd/RFUAV has 413 stars on GitHub.
- Where can I find RFUAV?
- kitoweeknd/RFUAV is on GitHub at https://github.com/kitoweeknd/RFUAV.