The Intelligent Transport System (ITS) not only extracts the features of vehicles via recognizing video signals, but also reduces the traffic jams, thereby, improve the vehicle pass ability of the highway entrance, and the efficiency of transportation. License plate recognition (LPR) is an important part of ITS. However, the license plate may be fake or be intentionally sheltered or smeared, which increases the difficulty of LPR. Considering that the logo information is difficult to change, we combine the logo information and license plate information to improve the reliability of vehicle identification. Based on this idea, a highway entrance vehicle logo recognition system is designed by using convolutional neural network(CNN) to detect and classify the vehicle logo in real time. We design a dataset named VL to train CNN and test the performance of our system.


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    Titel :

    A Highway Entrance Vehicle Logo Recognition System Based on Convolutional Neural Network


    Beteiligte:
    Yuxin, Mao (Autor:in) / Peifeng, Hao (Autor:in)


    Erscheinungsdatum :

    01.05.2019


    Format / Umfang :

    3285694 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch