| Title | Audio Based Drone Detection and Identification using Deep Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Al-Emadi, Sara, Al-Ali, Abdulla, Mohammad, Amr, Al-Ali, Abdulaziz |
| Conference Name | 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC) |
| Keywords | Acoustic Fingerprinting, Acoustic Fingerprints, Acoustics, artificial intelligence, audio recorded samples, audio recording, audio signal processing, autonomous aerial vehicles, composability, convolutional neural network, Deep Learning, deep learning techniques, deep neural network algorithms, drone, drone activities, drone audio clips, Drone Audio Dataset, drone detection, drone identification, drones, flying drones, Human Behavior, learning (artificial intelligence), machine learning, machine learning algorithms, malicious activities, multiprocedural process, Noise measurement, private properties, pubcrawl, recurrent neural nets, recurrent neural network, Recurrent neural networks, Resiliency, UAVs, unmanned aerial vehicles |
| Abstract | In recent years, unmanned aerial vehicles (UAVs) have become increasingly accessible to the public due to their high availability with affordable prices while being equipped with better technology. However, this raises a great concern from both the cyber and physical security perspectives since UAVs can be utilized for malicious activities in order to exploit vulnerabilities by spying on private properties, critical areas or to carry dangerous objects such as explosives which makes them a great threat to the society. Drone identification is considered the first step in a multi-procedural process in securing physical infrastructure against this threat. In this paper, we present drone detection and identification methods using deep learning techniques such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Convolutional Recurrent Neural Network (CRNN). These algorithms will be utilized to exploit the unique acoustic fingerprints of the flying drones in order to detect and identify them. We propose a comparison between the performance of different neural networks based on our dataset which features audio recorded samples of drone activities. The major contribution of our work is to validate the usage of these methodologies of drone detection and identification in real life scenarios and to provide a robust comparison of the performance between different deep neural network algorithms for this application. In addition, we are releasing the dataset of drone audio clips for the research community for further analysis. |
| DOI | 10.1109/IWCMC.2019.8766732 |
| Citation Key | al-emadi_audio_2019 |
Audio Based Drone Detection and Identification using Deep Learning