用频谱分析实时检测无人机，速度更快精度更高。
Real-Time Drone Detection in Event Cameras via Per-Pixel Frequency Analysis
- 基于非均匀傅里叶变换，从像素级时序中提取旋翼频率特征。
- 每帧仅2.39毫秒延迟，定位F1达90.89%，优于YOLO的66.74%。
- 无需大量数据训练，可快速调参，适合嵌入式实时系统。
从事件相机数据中检测高速移动目标（如无人机）极具挑战性，因其输入具有稀疏性和异步性。传统离散傅里叶变换（DFT）虽能识别周期信号（如旋转旋翼），但假设数据均匀采样，而事件相机不满足此条件。本文提出一种基于非均匀离散傅里叶变换（NDFT）的逐像素时序分析框架，命名为无人机谐波指纹检测（DDHF）。该方法通过纯解析手段识别旋翼在功率谱中形成的频率梳特征，实现可调且通用的实时无人机定位。在相同条件下与YOLO检测器对比，结果显示：DDHF平均定位F1分数达90.89%，每帧延迟仅2.39毫秒；而YOLO F1为66.74%，延迟12.40毫秒。由于采用纯解析技术，DDHF可在小数据上快速调参，易于解释，性能媲美深度学习方法。
原文摘要 · Abstract (English)
Detecting fast-moving objects, such as unmanned aerial vehicle (UAV), from event camera data is challenging due to the sparse, asynchronous nature of the input. Traditional Discrete Fourier Transforms (DFT) are effective at identifying periodic signals, such as spinning rotors, but they assume uniformly sampled data, which event cameras do not provide. We propose a novel per-pixel temporal analysis framework using the Non-uniform Discrete Fourier Transform (NDFT), which we call Drone Detection via Harmonic Fingerprinting (DDHF). Our method uses purely analytical techniques that identify the frequency signature of drone rotors, as characterized by frequency combs in their power spectra, enabling a tunable and generalizable algorithm that achieves accurate real-time localization of UAV. We compare against a YOLO detector under equivalent conditions, demonstrating improvement in accuracy and latency across a difficult array of drone speeds, distances, and scenarios. DDHF achieves an average localization F1 score of 90.89% and average latency of 2.39ms per frame, while YOLO achieves an F1 score of 66.74% and requires 12.40ms per frame. Through utilization of purely analytic techniques, DDHF is quickly tuned on small data, easily interpretable, and achieves competitive accuracies and latencies to deep learning alternatives.
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