Abstract
In recent years, the illicit use of unmanned aerial vehicles (UAVs) for deliveries in restricted area such as prisons became a significant security challenge. While numerous studies have focused on UAV detection or localization, little attention has been given to delivery events identification. This study presents the first acoustic package delivery detection algorithm using a ground-based microphone array. The proposed method estimates both the drone's propeller speed and the delivery event using solely acoustic features. A deep neural network detects the presence of a drone and estimates the propeller's rotation speed or blade passing frequency (BPF) from a mel spectrogram. The algorithm analyzes the BPFs to identify probable delivery moments based on sudden changes before and after a specific time. Results demonstrate a mean absolute error of the blade passing frequency estimator of 16 Hz when the drone is less than 150 meters away from the microphone array. The drone presence detection estimator has a accuracy of 97%. The delivery detection algorithm correctly identifies 96% of events with a false positive rate of 8%. This study shows that deliveries can be identified using acoustic signals up to a range of 100 meters.
Abstract (translated)
近年来，无人飞行器（UAV）在监狱等限制区域非法进行包裹递送已成为一个重要的安全挑战。尽管许多研究集中在无人机检测或定位上，但对递送事件识别的关注较少。本研究提出了首个基于地面麦克风阵列的声学包裹递送检测算法。该方法仅通过声学特征就能估算出无人机螺旋桨的速度和递送事件。 所提出的方法使用深度神经网络从梅尔频谱图中检测无人机的存在，并估计螺旋桨旋转速度或叶片通过频率（BPF）。算法会分析这些BPF，基于特定时间点前后突然变化来识别可能的递送时刻。当无人机距离麦克风阵列不到150米时，螺旋桨叶片通过频率估算器的平均绝对误差为16 Hz。无人机存在检测估计器具有97%的准确性。包裹递送算法正确地识別了96％的事件，并且误报率为8％。 研究表明，使用声学信号可以在距离麦克风阵列最多100米范围内识别出递送行为。
URL
https://arxiv.org/abs/2602.09991
