Audio-Based Drone Detection System Using FFT and Machine Learning Models

In recent years, the use of drones, also known as unmanned aerial vehicles (UAVs), has experienced a rapid increase due to their wide availability, compact size, low cost, and ease of operation. These devices have found applications in various areas, facilitating human work by covering large distances and operating in inaccessible or dangerous zones. However, their use has also been associated with malicious activities, such as property damage or threats to public security, which highlights the need to develop efficient and precise UAV detection systems. Although approaches based on neural networks have been proposed, they require large amounts of data for training and more computational resources for operation, which limits their applicability. In this study, we propose an alternative approach based on an analysis of audio features obtained through the fast Fourier transform (FFT) algorithm and classification using machine learning (ML) models. Our approach aims to detect the presence of drones using a minimal number of samples, meeting the requirements of efficiency, accuracy, robustness, low cost, and scalability necessary for a functional detection system.