Benchmarking Audio-based Deep Learning Models for Detection and Identification of Unmanned Aerial Vehicles

Abstract

Over the last few years, Unmanned Aerial Vehicles (UAVs) have become increasingly popular for both commercial and personal applications. As a result, security concerns in both physical and cyber domains have been raised, as a malicious UAV can be used for the jamming of nearby targets or even for carrying explosive assets. UAV detection and identification is a very important task for safety and security. In this regard, several techniques have been proposed for the detection and identification of UAVs, in general, through image, audio, radar, and RF based approaches. In this paper, we benchmark the detection and identification of UAVs via audio data from [1]. We benchmarked with widely used deep learning algorithms such as Deep Neural Networks (DNN), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), Convolutional Long Short Term Memory (CLSTM) and Transformer Encoders (TE). In addition to the dataset of [1], we collected our own diverse identification audio dataset and experimented with Deep Neural Networks (DNN). In a UAV detection task, our best model (LSTM) outperformed the best model of [1] (CRNN) by over 4% in accuracy, 2% in precision, 4% in recall and 4% in F1-score. In UAV identification task, our best model (LSTM) outperformed the best model of [1] (CNN) by over 5% in accuracy, 2% in precision, 4% in recall and 3% in F1-score.