Sequential Model-based Optimization Approach Deep Learning Model for Classification of Multi-class Traffic Sign Images

Autonomous vehicles are currently gaining popularity in the future mobility ecosystem. The development of autonomous driving systems is still challenging in the research area of image processing and signal processing. Extensive research work was conducted on various traffic sign datasets. It achieved respectable results, but a robust network structure is still needed to develop to improve the traffic sign recognition (TSR) system. In this research work, there is an alternative approach to designing deep learning models, which are implemented in TSR systems. The proposed deep learning model was also tested with different datasets to obtain the generalized model. The proposed model was based on a convolutional neural network (CNN), and Bayesian Optimization optimizes the model’s hyperparameters to find the best hyperparameters grid. After that, the optimized CNN model was used to classify the traffic sign images from three different datasets, including the German traffic sign recognition benchmark (GTSRB), the Belgium traffic sign classification (BTSC) dataset, and the Chinese traffic sign database, achieving the average accuracy scores of 99.57%, 99.15%, and 99.35%, respectively.

@article{Aung2023,
  title     = {Sequential Model-based Optimization Approach Deep Learning Model for Classification of Multi-class Traffic Sign Images},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {7},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Si Thu Aung and Jartuwat Rajruangrabin and Ekkarut Viyanit},
  doi       = {10.14569/IJACSA.2023.0140764},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140764}
}