Vehicle detection technology based on remote sensing images, as a new method of collecting traffic flow information, provides new ideas for traffic management. A feature-fusion-based convolutional neural network vehicle detection method is proposed. On the basis of image preprocessing, first use the VGG16 convolutional neural network to obtain multi-level features, and then use variable-scale stacking to obtain the basic feature layer to achieve the acquisition of deep convolution features, and then construct a feature pyramid to divide the basic feature layer operation, finally use the attention mechanism to fuse hierarchical information, and then efficiently extract vehicle features. In the example high-resolution remote sensing image vehicle automatic detection experiment, the vehicle automatic detection accuracy rate was 88.7%, and the false detection rate was 1.4%. The experiment shows that this model is better for automatic vehicle detection in high-resolution remote sensing images, especially in dense urban traffic scenes. good detection effect.


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    Titel :

    High-resolution remote sensing vehicle automatic detection based on feature fusion convolutional neural network


    Beteiligte:
    Li, Xin (Autor:in) / Guo, Kai (Autor:in) / Subei, Mutailifu (Autor:in) / Guo, Dudu (Autor:in)

    Kongress:

    International Conference on Computer Vision, Application, and Design (CVAD 2021) ; 2021 ; Sanya,China


    Erschienen in:

    Proc. SPIE ; 12155


    Erscheinungsdatum :

    21.12.2021





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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