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.
|