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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Drone Detection with Visual Transformer


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wu, Meiping (editor) / Niu, Yifeng (editor) / Gu, Mancang (editor) / Cheng, Jin (editor) / Wang, Xingjian (author) / Zhou, Chengwei (author) / Xie, Jiayang (author) / Yan, Chenggang (author) / Shi, Zhiguo (author)

    Conference:

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



    Publication date :

    2022-03-18


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Drone Detection with Visual Transformer

    Wang, Xingjian / Zhou, Chengwei / Xie, Jiayang et al. | TIBKAT | 2022


    Drone Detection with Visual Transformer

    Wang, Xingjian / Zhou, Chengwei / Xie, Jiayang et al. | British Library Conference Proceedings | 2022


    Onboard visual drone detection for drone chasing and collision avoidance

    Makirin, M. K. / Wastupranata, L. M. / Daffa, A. | TIBKAT | 2021


    Onboard visual drone detection for drone chasing and collision avoidance

    Makirin, M. K. / Wastupranata, L. M. / Daffa, A. | American Institute of Physics | 2021


    Object Detection in Drone Video with Temporal Attention Gated Recurrent Unit Based on Transformer

    Zihao Zhou / Xianguo Yu / Xiangcheng Chen | DOAJ | 2023

    Free access