Implementing Machine Learning Algorithms on GPUs for Real-Time Traffic Sign Classification
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}
}
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.
March 12, 2015 by hgpu
