The rapid growth of drones presents potential threats to public security and personal privacy, and it is vital to effectively detect the intruding drones. Prior work on visual-based drone detection using convolutional networks regards the drone detection task as a regression problem on a large set of human-defined components, i.e., proposals and anchors. These components bring a huge number of predictions to be selected, which pose the challenges to drone detection. In this paper, we propose a Deformable DETR-based drone detector with visual transformer, which eliminates the human-defined components to pursue high-accuracy detection performance. Specifically, to detect remote drones at a higher accuracy, the resolution of the features in backbone is enhanced. Meanwhile, two data augmentation methods including illumination jittering and multi-blurring are developed to cope with the time-varying illumination and the changeable weather, based on which the environmental robustness of the proposed detector is thus maintained. The field experiments are carried out, and it is demonstrated that a higher detection accuracy is achieved for the proposed drone detector.
Drone Detection with Visual Transformer
Lect. Notes Electrical Eng.
International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021
Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021) ; Kapitel : 264 ; 2689-2699
18.03.2022
11 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Drone Detection with Visual Transformer
TIBKAT | 2022
|Drone Detection with Visual Transformer
British Library Conference Proceedings | 2022
|Onboard visual drone detection for drone chasing and collision avoidance
American Institute of Physics | 2021
|Object Detection in Drone Video with Temporal Attention Gated Recurrent Unit Based on Transformer
DOAJ | 2023
|