Improved Deep Learning Performance for Real-Time Traffic Sign Detection and Recognition Applicable to Intelligent Transportation Systems

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.

@article{BARODI2022,
  title     = {Improved Deep Learning Performance for Real-Time Traffic Sign Detection and Recognition Applicable to Intelligent Transportation Systems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {5},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Anass BARODI and Abderrahim Bajit and Abdelkarim ZEMMOURI and Mohammed Benbrahim and Ahmed Tamtaoui},
  doi       = {10.14569/IJACSA.2022.0130582},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130582}
}