Two Dimensional Convolutional Neural Network Frameworks Using Acoustic Nodes for UAV Security Applications
Two Dimensional Convolutional Neural Network Frameworks Using Acoustic Nodes for UAV Security Applications
Unmanned Aerial Vehicles (UAVs) have become increasingly popular to the public with a multitude of associated applications in real life. However, malicious applications of UAVs, such as terrorism and air transport disturbance may occur with devastating consequences. Therefore, counter-UAV (C-UAV) systems are required to efficiently address these undesired situations. Artificial intelligence approaches, such as deep neural networks (DNNs), are deemed crucial for high accuracy detection and real-time response of the next generation C-UAV systems. UAV detection based on acoustic sensors has received a great research interest over the past years. Ηowever, the performance of the proposed solutions may vary in different environments since sound signals from UAVs are multi-source and unstructured. In this work, three different 2D Convolutional Neural Networks (CNNs) using short-time Fourier transform spectrograms as input, for UAV binary classification in real-world settings, are compared. The results confirm that 2D CNNs, along with data augmentation techniques in the image domain, can capture the spatio-temporal information of the UAV sound signals, even in noisy environments, and obtain a 95.35% macro average F1-Score in a dataset collected from two different environments.
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