| 基于卷积神经网络的交通标志识别方法 |
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| 引用本文： | 朱永佳,张静.基于卷积神经网络的交通标志识别方法[J].上海师范大学学报(自然科学版),2018,47(5):617-621. |
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| 作者姓名： | 朱永佳 张静 |
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| 作者单位： | 上海师范大学 信息与机电工程学院, 上海 200234,上海师范大学 信息与机电工程学院, 上海 200234 |
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| 摘 要： | 针对卷积神经网络(CNN)在交通标志识别过程中出现的梯度弥散而引起的识别率低的问题,给出了基于改进CNN结构的交通标志识别方法.实验结果表明:该方法能够有效提高识别精度,防止梯度弥散.
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| 关 键 词： | 卷积神经网络(CNN) 交通标志识别 深度学习 |
| 收稿时间： | 2017/6/20 0:00:00 |
Traffic sign recognition algorithm based on improved convolution neural network |
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| ZHU Yongjia and ZHANG Jing.Traffic sign recognition algorithm based on improved convolution neural network[J].Journal of Shanghai Normal University(Natural Sciences),2018,47(5):617-621. |
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| Authors: | ZHU Yongjia and ZHANG Jing |
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| Institution: | College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China and College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China |
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| Abstract: | For the problem of low traffic sign recognition rate due to gradient diffusion in convolution neural network(CNN), an improved convolution neural network was proposed. The experiment results showed that the improved method could increase the recognition accuracy effectively and prevent gradient vanishing. |
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| Keywords: | convolution neural network (CNN) traffic sign recognition deep learning |
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