中文摘要：

交通标志识别作为典型的机器视觉应用,已有多种机器视觉算法得到广泛的应用。卷积神经网络能够避免显式的人工特征提取过程,因此本文引入卷积神经网络为交通标志进行识别研究,并与BP神经网络、支持向量机进行对比实验,通过对实验结果的理解与分析,可以得出卷积神经网络在识别率及训练速度上均显著高于另两种算法,并能取得最佳的识别效果。

英文摘要：

Traffic signs recognition as a typical machine vision application,a variety of machine vision algorithms have been widely used.Convolutional neural network can avoid explicit artificial feature extraction process.Therefore,this thesis introduces convolutional neural network for traffic sign recognition research,and comparative experiments with BP neural network,support vector machine,through the understanding and analysis of the experimental results,it can be derived from the convolution neural network in recognition rate and the training speed were significantly higher than those of the other two algorithm, and can achieve the best effect of recognition.