Author(s)

Yuanzhou Wei¹, Meiyan Gao^1*, Jun Xiao^1*, Chixu Liu^2*, Yuanhao Tian^3*, Ya He^4*

¹College of Engineering and Computing, Florida International University, Miami, USA.
²College of Intelligent Equipment, Shandong University of Science and Technology, Qingdao, China.
³Steven J. Green School of International & Public Affairs, Florida International University, Miami, USA.
⁴School of Economics, Capital University of Economics and Business, Beijing, China.

Traffic sign recognition is an important task in intelligent transportation systems, which can improve road safety and reduce accidents. Algorithms based on deep learning have achieved remarkable results in traffic sign recognition in recent years. In this paper, we build traffic sign recognition algorithms based on ResNet and CNN models, respectively. We evaluate the proposed algorithm on public datasets and compare. We first use the dataset of traffic sign images from Kaggle. And then designed ResNet-based and CNN-based architectures that can effectively capture the complex features of traffic signs. Our experiments show that our ResNet-based model achieves a recognition accuracy of 99% on the test set, and our CNN-based model achieves a recognition accuracy of 98% on the test set. Our proposed approach has the potential to improve traffic safety and can be used in various intelligent transportation systems.

CNN, Traffic Sign, ResNet, Recognition, Neural Network, TensorFlow

Wei, Y. , Gao, M. , Xiao, J. , Liu, C. , Tian, Y. and He, Y. (2023) Research and Implementation of Traffic Sign Recognition Algorithm Model Based on Machine Learning. Journal of Software Engineering and Applications, 16, 193-210. doi: 10.4236/jsea.2023.166011.