Traffic sign recognition constitutes a crucial component in the realm of autonomous driving. To address the issue of sub-optimal recognition accuracy, we propose an innovative method premised on convolutional neural networks. This method enhances recognition accuracy through data augmentation and the refinement of deep learning networks, thereby successfully recognizing traffic signs. The enhanced model underwent training and validation on the German Traffic Sign Recognition Benchmark (GTSRB), yielding a recognition accuracy of 97.76% on the test set. The experimental results illustrate that our approach can effectively and accurately recognize traffic signs in complex environments, rendering it a valuable technical asset for subsequent research in this field.


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    Title :

    Traffic Sign Recognition Method Based on Convolutional Neural Network


    Contributors:
    Li, Jitong (author) / Chen, Yuguang (author) / Lin, Honghao (author) / Chen, Fang (author) / Yang, Bin (author)

    Conference:

    24th COTA International Conference of Transportation Professionals ; 2024 ; Shenzhen, China


    Published in:

    CICTP 2024 ; 490-499


    Publication date :

    2024-12-11




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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