Aiming at the problems of low detection accuracy and large computing resource consumption of existing Unmanned Aerial Vehicle (UAV) detection algorithms for anti-UAV, this paper proposes a lightweight UAV swarm detection method based on You Only Look Once Version X (YOLOX). This method uses depthwise separable convolution to simplify and optimize the network, and greatly simplifies the total parameters, while the accuracy is only partially reduced. Meanwhile, a Squeeze-and-Extraction (SE) module is introduced into the backbone to improve the model′s ability to extract features; the introduction of a Convolutional Block Attention Module (CBAM) in the feature fusion network makes the network pay more attention to important features and suppress unnecessary features. Furthermore, Distance-IoU (DIoU) is used to replace Intersection over Union (IoU) to calculate the regression loss for model optimization, and data augmentation technology is used to expand the dataset to achieve a better detection effect. The experimental results show that the mean Average Precision (mAP) of the proposed method reaches 82.32%, approximately 2% higher than the baseline model, while the number of parameters is only about 1/10th of that of YOLOX-S, with the size of 3.85 MB. The proposed approach is, thus, a lightweight model with high detection accuracy and suitable for various edge computing devices.


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    Title :

    A Lightweight Uav Swarm Detection Method Integrated Attention Mechanism


    Contributors:
    Chuanyun Wang (author) / Linlin Meng (author) / Qian Gao (author) / Jingjing Wang (author) / Tian Wang (author) / Xiaona Liu (author) / Furui Du (author) / Linlin Wang (author) / Ershen Wang (author)


    Publication date :

    2022




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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