Deep learning for unmanned aerial vehicles detection: A review
Al-lQubaydhi N. Alenezi A. Alanazi T. Senyor A. Alanezi N. Alotaibi B. Alotaibi M. Razaque A. Hariri S.
February 2024Elsevier Ireland Ltd
Computer Science Review
2024#51
As a new type of aerial robotics, drones are easy to use and inexpensive, which has facilitated their acquisition by individuals and organizations. This unequivocal and widespread presence of amateur drones may cause many dangers, such as privacy breaches by reaching sensitive locations of authorities and individuals. In this paper, we summarize the performance-affecting factors and major obstacles to drone use and provide a brief background of deep learning. Then, we summarize the types of UAVs and the related unethical behaviors, safety, privacy, and cybersecurity concerns. Then, we present a comprehensive literature review of current drone detection methods based on deep learning. This area of research has arisen in the last two decades because of the rapid advancement of commercial and recreational drones and their combined risk to the safety of airspace. Various deep learning algorithms and their frameworks with respect to the techniques used to detect drones and their areas of applications are also discussed. Drone detection techniques are classified into four categories: visual, radar, acoustics, and radio frequency-based approaches. The findings of this study prove that deep learning-based detection and classification of drones looks promising despite several challenges. Finally, we provide some recommendations to meet future expectations.
Convolutional neural network , Deep learning , Drone detection , Recurrent neural network , Unmanned aerial vehicle
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Department of Information Technology, University of Tabuk, Tabuk, Saudi Arabia
Sensor Networks and Cellular Systems Research Center, University of Tabuk, Tabuk, Saudi Arabia
Department of Computer Science, Shaqra University, Shaqra, Saudi Arabia
Department of Computer Engineering & Information Security, IITU, Almaty, 050000, Kazakhstan
Department of Electrical and Computer Engineering, The University of Arizona, Tucson, 85719, AZ, United States
Department of Information Technology
Sensor Networks and Cellular Systems Research Center
Department of Computer Science
Department of Computer Engineering & Information Security
Department of Electrical and Computer Engineering
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