Survey and Comparative Study for Drone Detection Using Deep Learning - Tan, Ziya; Karakose, Mehmet (Institute of Electrical and Electronics Engineers Inc., 2022)
The widespread use of drones and the reduction of costs have made studies with drones popular. Especially the studies using artificial intelligence are followed carefully. The increase in these studies has paved the way for the integration of artificial intelligence algorithms such as computer vision, object tracking, and object detection into drones to perform more complex tasks autonomously. In addition, unmanned aerial vehicles are used in many useful tasks to eliminate illegal security threats such as border violations and drug trafficking. For this reason, the importance of drones is increasing day by day. In this article, the articles related to drone detection using state-of-art deep learning algorithms in the last 3 years have been reviewed and compiled. In particular, the methods used, suggested approaches, analysis methods, and the results obtained in these articles are summarized. In terms of the algorithms used in the reviewed articles, it is seen that it is preferred more frequently due to the success of radio signals and the success of one-stage detectors in terms of detection methods. © 2022 IEEE.
2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022 -- 25 October 2022 through 26 October 2022 -- Virtual, Online -- 186761
Keywords: Cnn; Deep learning; drone detection; object detection; Yolo