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المؤلفAl-Emadi, Sara
المؤلفAl-Ali, Abdulla
المؤلفAl-Ali, Abdulaziz
تاريخ الإتاحة2023-11-25T07:30:05Z
تاريخ النشر2021-08-01
اسم المنشورSensors
المعرّفhttp://dx.doi.org/10.3390/s21154953
الاقتباسAl-Emadi, S., Al-Ali, A., & Al-Ali, A. (2021). Audio-based drone detection and identification using deep learning techniques with dataset enhancement through generative adversarial networks. Sensors, 21(15), 4953.‏
الرقم المعياري الدولي للكتاب14248220
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85110553859&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/49638
الملخصDrones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones.
اللغةen
الناشرMDPI
الموضوعAcoustic fingerprinting
Artificial intelligence
Convolutional neural network CNN
Convolutional recurrent neural network CRNN
Deep learning
Drone
Drone audio dataset
Drone detection
Drone identification
Generative adversarial networks GAN
Machine learning
Recurrent neural network RNN
UAV
العنوانAudio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks
النوعArticle
رقم العدد15
رقم المجلد21
dc.accessType Open Access


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