@inproceedings{c966ca0f629e489e9aee20f3c2e745b2,
title = "Pre-trained Deep Learning Networks for Advanced Visible Imagery Drone Detection and Recognition",
abstract = "In this paper, we introduce a state-of-the-art deep learning technique designed to accurately differentiate between drones and birds. This technique is particularly effective in reducing hazards associated with unauthorized drones, especially in airport environments where such drones can cause significant flight disruptions. Our approach involves the utilization of a meticulously compiled image dataset for testing, yielding results that surpass previous detection methods outlined in existing literature. Among the models evaluated, ResNet18 emerges as a standout, achieving an impressive average precision (AP) of 0.739 in medium area ratios. A key feature of our method is its ability not only to detect drones but also to precisely distinguish them from birds. The dataset employed in this research is derived from the publicly accessible real-world data of the 2020 Drone vs. Bird Detection Challenge.",
keywords = "Bird, Classification, Drone, Drone detection, deep networks, transfer learning",
author = "\{Al Dawasari\}, \{Hassan J.\} and Muhammad Bilal and Muhammad Moinuddin and Kamran Arshad and Khaled Assaleh",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023 ; Conference date: 22-12-2023 Through 23-12-2023",
year = "2023",
doi = "10.1109/CICN59264.2023.10402291",
language = "English",
series = "Proceedings - 2023 15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "316--320",
booktitle = "Proceedings - 2023 15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023",
address = "United States",
}