Traffic sign recognition (TSR) is an important and challenging task for intelligent transportation systems. We describe the details of our model's architecture for TSR and suggest a hinge loss stochastic gradient descent (HLSGD) method to train convolutional neural networks (CNNs). Our CNN consists of three stages (70–110–180) with 1 162 284 trainable parameters. The HLSGD is evaluated on the German Traffic Sign Recognition Benchmark, which offers a faster and more stable convergence and a state-of-the-art recognition rate of 99.65%. We write a graphics processing unit package to train several CNNs and establish the final classifier in an ensemble way.


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    Title :

    Traffic Sign Recognition With Hinge Loss Trained Convolutional Neural Networks


    Contributors:
    Jin, Junqi (author) / Fu, Kun (author) / Zhang, Changshui (author)


    Publication date :

    2014-10-01


    Size :

    1415220 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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