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.

中文翻译：

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无人机检测的深度学习：综述

无人机作为一种新型空中机器人，使用方便且价格低廉，方便了个人和组织购买。业余无人机的这种明确而广泛的存在可能会造成许多危险，例如到达当局和个人的敏感地点而侵犯隐私。在本文中，我们总结了无人机使用的性能影响因素和主要障碍，并提供了深度学习的简要背景。然后，我们总结了无人机的类型以及相关的不道德行为、安全、隐私和网络安全问题。然后，我们对当前基于深度学习的无人机检测方法进行了全面的文献综述。由于商业和娱乐无人机的快速发展及其对空域安全的综合风险，这一研究领域在过去二十年中兴起。还讨论了与用于检测无人机的技术及其应用领域有关的各种深度学习算法及其框架。无人机检测技术分为四类：视觉、雷达、声学和基于射频的方法。这项研究的结果证明，尽管面临一些挑战，基于深度学习的无人机检测和分类看起来还是很有前景的。最后，我们提供一些建议以满足未来的期望。