With the in-depth development of intelligent transportation, traffic sign recognition has attracted widespread attention as an essential part of intelligent transportation. This paper studies several machine learning methods for traffic sign recognition. Through comparative analysis, it is found that Convolutional Neural Network (CNN) is superior to Support Vector Machine (SVM) and K Nearest Neighbor (KNN) methods in recognizing traffic signs. And adding Gaussian noise to the image data for enhancement can further improve the accuracy of applying a Convolutional Neural Network to identify traffic signs. The accuracy of applying a Convolutional Neural Network to identify traffic signs is 99.2%. After adding Gaussian noise with a mean of 0 and a standard deviation of 1 to the image set, the accuracy of applying a Convolutional Neural Network to identify traffic signs was increased to 99.6%. We also compared the CNN-based traffic signs recognition experiment in this paper with the experiments of two other scholars. Our experiment has higher accuracy in a particular data range and environment.


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    Titel :

    Research on traffic sign recognition based on several machine learning methods


    Beteiligte:
    Qi, Hao (Autor:in) / Qin, Zhuohang (Autor:in) / Yang, Yue (Autor:in) / Liu, Siyuan (Autor:in) / Ren, Huilin (Autor:in)

    Kongress:

    Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022) ; 2022 ; Hulun Buir,China


    Erschienen in:

    Proc. SPIE ; 12475


    Erscheinungsdatum :

    2022-11-23





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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