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王晓斌1; 黄金杰1; 刘文举2
Source Publication计算机应用
Other AbstractIn 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 LayerWise 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.

Keyword卷积神经网络 批量归一化 贪婪预训练 支持向量机
Document Type期刊论文
Corresponding Author刘文举
Affiliation1.哈尔滨理工大学 自动化学院
2.中国科学院 自动化研究所
Recommended Citation
GB/T 7714
王晓斌,黄金杰,刘文举. 基于优化卷积神经网络结构的交通标志识别[J]. 计算机应用,2017,37(2):530-534.
APA 王晓斌,黄金杰,&刘文举.(2017).基于优化卷积神经网络结构的交通标志识别.计算机应用,37(2),530-534.
MLA 王晓斌,et al."基于优化卷积神经网络结构的交通标志识别".计算机应用 37.2(2017):530-534.
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