A Review on Intrusion Drone Target Detection Based on Deep Learning

In order to solve the problems of disrupting aviation order, stealing private information by the abuse of commercial small drones, even terrorist attacks caused by drones, many drone target detection algorithms based on deep learning have been proposed in recent years, but due to the lack of large-scale public datasets, most of the existing researches are based on self-made datasets, the algorithms lack comparative analysis. In this paper, we summarize and analyze the current research status of intrusion drone target detection based on deep learning. The paper summarizes the existing target detection algorithms based on deep learning, aiming at the problem that public drone target datasets are insufficient and difficult to obtain, it summarizes the currently publicly available drone datasets, then summarizes the intrusion drone target detection algorithms based on deep learning in recent years. Finally, the detection performance of some algorithms under different public datasets is compared, the difficulties in current research and direction of next research are analyzed.