Acoustic sensing on multi-rotor UAV for target detection using a convolutional neural network

Kevin McKenzie; Eddie Jacobs; Alf Ramirez; Joseph Conroy; Thomas Watson

doi:10.1117/12.3013877

8 June 2024 Acoustic sensing on multi-rotor UAV for target detection using a convolutional neural network

Kevin McKenzie, Eddie Jacobs, Alf Ramirez, Joseph Conroy, Thomas Watson

Proceedings Volume 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II; 1303504 (2024) https://doi.org/10.1117/12.3013877
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

This research presents an in-depth investigation into the application of Convolutional Neural Networks (CNN) for acoustic remote sensing on multi-rotor UAVs, with a specific focus on detecting large vehicles on the ground. We used a multi-rotor UAV equipped with a custom audio recorder, calibrated microphones, and uniquely designed microphone mounts for data collection. We explored optimal features for training our CNN, experimented with different normalization techniques, and examined their synergy between various activation functions. The study further explores the fine-tuning of model parameters to enhance detection performance and reliability. The outcome was a CNN model, trained with a combination of both real-world and synthetic data, demonstrating a proficient capability in target detection.

Conference Presentation

Citation Download Citation

Kevin McKenzie, Eddie Jacobs, Alf Ramirez, Joseph Conroy, and Thomas Watson "Acoustic sensing on multi-rotor UAV for target detection using a convolutional neural network", Proc. SPIE 13035, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications II, 1303504 (8 June 2024); https://doi.org/10.1117/12.3013877

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KEYWORDS

Unmanned aerial vehicles

Acoustics

Convolutional neural networks

Target detection

Data modeling

Education and training

Remote sensing

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