基于优化卷积神经网络结构的交通标志识别

现有算法对交通标志进行识别时，存在训练时间短但识别率低，或识别率高但训练时间长的问题。为此，综合批量归一化（BN）方法、逐层贪婪预训练（GLP）方法，以及把分类器换成支持向量机（SVM）这三种方法对卷积神经网络（CNN）结构进行优化，提出基于优化CNN结构的交通标志识别算法。其中：BN方法可以用来改变中间层的数据分布情况，把卷积层输出数据归一化为均值为0、方差为1，从而提高训练收敛速度，减少训练时间；GLP方法则是先训练第一层卷积网络，训练完把参数保留，继续训练第二层，保留参数，直到把所有卷积层训练完毕，这样可以有效提高卷积网络识别率；SVM分类器只专注于那些分类错误的样本，对已经分类正确的样本不再处理，从而提高了训练速度。使用德国交通标志识别数据库进行训练和识别，新算法的训练时间相对于传统CNN训练时间减少了20.67%，其识别率达到了98.24%。所提算法通过对传统CNN结构进行优化，极大地缩短了训练时间，并具有较高的识别率。

Traffic sign recognition based on optimized convolutional neural network architecture

In the existing algorithms for traffic sign recognition, sometimes the training time is short but the recognition rate is low, and other times the recognition rate is high but the training time is long. To resolve these problems, the Convolutional Neural Network (CNN) architecture was optimized by using Batch Normalization (BN) method, Greedy Layer-Wise Pretraining (GLP) method and replacing classifier with Support Vector Machine (SVM), and a new traffic sign recognition algorithm based on optimized CNN architecture was proposed. BN method was used to change the data distribution of the middle layer, and the output data of convolutional layer was normalized to the mean value of 0 and the variance value of 1, thus accelerating the training convergence and reducing the training time. By using the GLP method, the first layer of convolutional network was trained with its parameters preserved when the training was over, then the second layer was also trained with the parameters preserved until all the convolution layers were trained completely. The GLP method can effectively improve the recognition rate of the convolutional network. The SVM classifier only focused on the samples with error classification and no longer processed the correct samples, thus speeding up the training. The experiments were conducted on Germany traffic sign recognition benchmark, the results showed that compared with the traditional CNN, the training time of the new algorithm was reduced by 20.67%, and the recognition rate of the new algorithm reached 98.24%. The experimental results prove that the new algorithm greatly shortens the training time and reached a high recognition rate by optimizing the structure of the traditional CNN.