Acoustic Drone Package Delivery Detection
Fran\c{c}ois Marcoux, Fran\c{c}ois Grondin

TL;DR
This paper introduces an acoustic detection method for identifying drone delivery events using ground microphones, achieving high accuracy and enabling detection within 100 meters.
Contribution
It presents the first acoustic algorithm to detect drone delivery events and estimate propeller speed using deep neural networks and acoustic features.
Findings
97% accuracy in drone presence detection
96% correct delivery event identification
Delivery detection effective up to 100 meters
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…
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Taxonomy
TopicsUAV Applications and Optimization · Fire Detection and Safety Systems · Advanced Neural Network Applications
