FHSS | Frontiers in Humanities and Social Sciences
← Back to Volume 4, Issue 4

Research on Road Sign Recognition Method for Autonomous Vehicles Based on Convolutional Neural Networks

Yuqi Yang, Qixiang Chen, Lei Zhang, Xiaoyi Shi
Vol. 4, No. 4, pp. 84-92, 2024 DOI: 10.54691/vrp9z656 April 27, 2024
Download PDF
Abstract.With the rapid development of autonomous driving technology, road sign recognition has become an important link to ensure the safety of autonomous vehicles. This paper proposes a road sign recognition method for autonomous vehicles based on Convolutional Neural Networks (CNN), aiming to improve the accuracy and efficiency of road sign recognition by using CNN. Firstly, the background and significance of this issue were introduced, including the development of autonomous vehicles and their demand for road sign recognition technology. The focus was on the application of convolutional neural networks in road sign recognition. This article provides a detailed introduction to the dataset used, the designed convolutional neural network model, and data preprocessing methods. In order to further improve accuracy, methods such as data augmentation and image enhancement were adopted. The research results of this paper indicate that the road sign recognition method for autonomous vehicles based on convolutional neural networks has high accuracy and efficiency, providing important support for the practical application of autonomous driving technology.
Keywords. Autonomous vehicles, Road sign recognition, Convolutional neural networks, Deep learning, Data preprocessing.
APA Citation Yuqi Yang, Qixiang Chen, Lei Zhang, Xiaoyi Shi (2024). Research on Road Sign Recognition Method for Autonomous Vehicles Based on Convolutional Neural Networks. Frontiers in Humanities and Social Sciences, 4(4), 84-92. https://doi.org/10.54691/vrp9z656

References

  1. ] Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Ferguson, R.,&LeCun, Y. (2013) Overfeature: Integrated recognition, localization, and detection using convolutional networks In Proceedings of the 2013 IEEE International Conference on Computer Vision, 213-220.
  2. Houben, S., Stallkamp, J.,&Salmen, J. (2013) Detection of traffic signs in real world images: The German Traffic Sign Detection Benchmark. In Proceedings of the 2013 IEEE International Joint Conference on Neural Networks, 1-8.
  3. Ciresan, D.,&Meier, U. (2012) Multi column deep neural networks for traffic sign classification Neural Networks, 32, 333-338.
  4. Chen, H., Su, Y., Wang, G., Fang, H.,&Wang, S. (2015) Deep learning for understanding traffic signs: A survey IEEE Transactions on Intelligent Transportation Systems, 16 (6), 3087-3097.
  5. Wu, H., Veeraraghavan, A.,&Wang, Y. (2018) Traffic sign recognition: How transferable are features in deep neural networks? Journal of Computing and Information Science in Engineering, 18 (4), 041003.