In order to solve the problem of vehicle congestion in smart cities, this paper proposes a new network model structure (VGG8-1-CapsNet) to classify the types of vehicles. First, the first 8 convolutional layers of VGG16 and one convolutional layer are combined to form the feature extraction layer of the network model. Secondly, the obtained features are input into the capsule network, and the three-dimensional spatial feature conversion is performed on the extracted features. Finally, the dynamic routing algorithm is used to output, so as to achieve the purpose of vehicle classification. The experiment-al results show that the model has 98.98% accuracy on the expanded BIT-Vehicle, and the training speed is faster.


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    Titel :

    Vehicle type classification under traffic monitoring based on improved capsule network


    Beteiligte:
    Yu, Xiaokang (Autor:in) / Zhang, Weizhong (Autor:in) / Wang, Ying (Autor:in) / Huang, Zhikai (Autor:in) / Hong, Xin (Autor:in) / Jiang, Bowen (Autor:in)

    Kongress:

    2nd International Conference on Internet of Things and Smart City (IoTSC 2022) ; 2022 ; Xiamen,China


    Erschienen in:

    Proc. SPIE ; 12249


    Erscheinungsdatum :

    06.05.2022





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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