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22 November 2022 Research on traffic sign recognition based on several machine learning methods
Hao Qi, Zhuohang Qin, Yue Yang, Siyuan Liu, Huilin Ren
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Proceedings Volume 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022); 124751S (2022) https://doi.org/10.1117/12.2659407
Event: Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 2022, Hulun Buir, China
Abstract
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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Hao Qi, Zhuohang Qin, Yue Yang, Siyuan Liu, and Huilin Ren "Research on traffic sign recognition based on several machine learning methods", Proc. SPIE 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 124751S (22 November 2022); https://doi.org/10.1117/12.2659407
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KEYWORDS
Image enhancement
Machine learning
Convolutional neural networks
Neural networks
Image processing
Data modeling
Machine vision
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