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.


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    Titel :

    Drone Detection with Visual Transformer


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wu, Meiping (Herausgeber:in) / Niu, Yifeng (Herausgeber:in) / Gu, Mancang (Herausgeber:in) / Cheng, Jin (Herausgeber:in) / Wang, Xingjian (Autor:in) / Zhou, Chengwei (Autor:in) / Xie, Jiayang (Autor:in) / Yan, Chenggang (Autor:in) / Shi, Zhiguo (Autor:in)

    Kongress:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Erscheinungsdatum :

    18.03.2022


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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