Traffic sign classification with dataset augmentation and convolutional neural network

Qing Tang; Laksono Kurnianggoro; Kang-Hyun Jo

doi:10.1117/12.2304707

10 April 2018 Traffic sign classification with dataset augmentation and convolutional neural network

Qing Tang, Laksono Kurnianggoro, Kang-Hyun Jo

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106152W (2018) https://doi.org/10.1117/12.2304707
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

Abstract

This paper presents a method for traffic sign classification using a convolutional neural network (CNN). In this method, firstly we transfer a color image into grayscale, and then normalize it in the range (-1,1) as the preprocessing step. To increase robustness of classification model, we apply a dataset augmentation algorithm and create new images to train the model. To avoid overfitting, we utilize a dropout module before the last fully connection layer. To assess the performance of the proposed method, the German traffic sign recognition benchmark (GTSRB) dataset is utilized. Experimental results show that the method is effective in classifying traffic signs.

Citation Download Citation

Qing Tang, Laksono Kurnianggoro, and Kang-Hyun Jo "Traffic sign classification with dataset augmentation and convolutional neural network", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106152W (10 April 2018); https://doi.org/10.1117/12.2304707

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