期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：5
DOI：10.14569/IJACSA.2022.0130582
语种：English
出版社：Science and Information Society (SAI)
摘要：In this paper, we improve the performance of Deep Learning (DL) by creating a robust and efficient Convolutional Neural Network (CNN) model. This CNN model will be subjected to detecting and recognizing traffic signs in real-time. We apply several techniques; the first is pre-processing, which includes conversion of color space RGB, then equalization and normalization histogram of the image dataset according to Computer Vision (CV) tools. The second is devoted to Artificial Intelligence (AI), which needs the right choice of a neural layer such convolution layer, or dropout layer, with powerful optimizer as Adam and activation functions such as ReLU and SoftMax. Also, we use the technique of augmentation dataset which characterizes by the function of batch size for each epoch. The results obtained is very satisfactory, the prediction at the average is equal to 98%, which encourages this approach to the integration in Intelligent Transportation Systems (ITS) in the automotive sector.
