The misuse of drones can jeopardize public safety and privacy. The detection and catching of intruding drones are crucial and urgent issues to be investigated. This work proposes VDTNet, an accurate, lightweight, and fast network for visually detecting and tracking intruding drones. We first incorporate an SPP module into the first head of YOLOv4 to enhance detection accuracy. Model compression is utilized to shrink the model size and concurrently speed up inference. We then propose and insert an SPPS module and a ResNeck module into the neck, and introduce an effective attention module for the backbone to compensate for the accuracy drop brought on by compression. With the above strategies, we present the accurate and compact VDTNet with a model size of merely 3.9 MB, ensuring low computational cost and fast detection and tracking performance in real time. Extensive experiments on four challenging public datasets show that our proposed network outperforms state-of-the-art approaches. In real-world scenarios, the comparative ground-to-air detection testing proves the generalization ability of the VDTNet, and we further demonstrate the portability and practicability of the network by deploying it on drone onboard edge-computing devices for air-to-air real-time detection of the intruding drones.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    VDTNet: A High-Performance Visual Network for Detecting and Tracking of Intruding Drones


    Contributors:
    Zhou, Xunkuai (author) / Yang, Guidong (author) / Chen, Yizhou (author) / Li, Li (author) / Chen, Ben M. (author)


    Publication date :

    2024-08-01


    Size :

    3576002 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Device for detecting intruding objects, and method for detecting intruding objects

    YASUGI MAKOTO / NISHIMURA HIROFUMI | European Patent Office | 2016

    Free access

    SYSTEM AND METHODS OF DETECTING INTRUDING OBJECT

    MICHAEL STEVEN FELDMANN / FRANK SAGGIO III / TIMOTHY JOHN PAASCHE et al. | European Patent Office | 2016

    Free access