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Implementing Machine Learning Algorithms on GPUs for Real-Time Traffic Sign Classification

Dashiell Bodington, Eric Greenstein, Matthew Hu
Department of Electrical Engineering, Stanford University
Stanford University, CS 229 Machine Learning project, 2014

@article{bodington2014implementing,

   title={Implementing Machine Learning Algorithms on GPUs for Real-Time Traffic Sign Classification},

   author={Bodington, Dashiell and Greenstein, Eric and Hu, Matthew},

   year={2014}

}

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This paper investigates traffic sign classification, which is an important problem to solve for autonomous driving. Linear discriminant analysis and convolutional neural networks achieved an accuracy of 98.25% and 98.75% respectively when classifying eight different types of traffic signs. The CNN was implemented on a GPU for real-time traffic sign classification: testing time for the CNN on a GPU was 4 ms/image, which was 7.5x as fast as running LDA on a CPU and 60.2x as fast as running CNN on a CPU. Additionally, different types of classification errors and the effects of adding a new sign to the dataset were explored.
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