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

Yuqi  Yang; Qixiang  Chen; Lei  Zhang; Xiaoyi  Shi

doi:10.54691/vrp9z656

Authors

Yuqi Yang
Qixiang Chen
Lei Zhang
Xiaoyi Shi

DOI:

https://doi.org/10.54691/vrp9z656

Keywords:

Autonomous vehicles, Road sign recognition, Convolutional neural networks, Deep learning, Data preprocessing.

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.

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