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• The proposed DDHF method achieves an average localization F1 score of 90.89% for drone detection using event cameras.result 95%
• DDHF processes frames with an average latency of 2.39ms, making it suitable for real-time applications.result 95%
• DDHF outperforms YOLO-based detection in both accuracy and latency for drone detection from event camera data.result 90%
• Traditional Discrete Fourier Transforms are ineffective for event camera data because they assume uniformly sampled data.technical 90%
• The DDHF method uses Non-uniform Discrete Fourier Transform for per-pixel temporal analysis to detect drone rotor frequency signatures.method 90%
• DDHF identifies drone rotors by detecting frequency combs in their power spectra, which serve as harmonic fingerprints.method 85%
• DDHF can be quickly tuned on small datasets due to its purely analytical nature, unlike deep learning methods.method 85%
• YOLO-based detection achieves only 66.74% F1 score and requires 12.40ms per frame for the same drone detection task.result 95%

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