Deep learning techniques for advanced drone detection systems: a comprehensive review of techniques, challenges and future directions

The widespread use of Unmanned Aerial Vehicles (UAVs), commonly known as drones, across various sectors, such as civilian, commercial, and military operations, has created significant challenges in ensuring security, safety, and privacy. This paper provides a comprehensive review of the latest advancements in drone detection systems leveraging deep learning techniques, covering the period from 2020 to 2024. It critically evaluates both optical (visible light and thermal infrared) and non-optical (radio frequency, radar, and acoustic) detection methodologies. The analysis includes cutting-edge models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), focusing on their application in drone detection. Key challenges like real-time processing, environmental interference, and differentiation between drones and similar objects are examined. Potential solutions, including sensor fusion, attention mechanisms, and the integration of emerging technologies such as the Internet of Things (IoT) and 5G networks, are discussed in detail. The paper concludes with future research directions to enhance drone detection systems' robustness, scalability, and accuracy, particularly in complex and dynamic environments. This review offers valuable insights for researchers and industry professionals working towards next-generation drone detection technologies.