标题：
Design and optimization of the model for traffic signs classification based on convolutional neural networks
点击次数：
所属单位：
自动化学院
发表刊物：
Lect. Notes Comput. Sci.
摘要：
Recently, convolutional neural networks (CNNs) demonstrate state-of-the-art performance in computer vision such as classification, recognition and detection. In this paper, a traffic signs classification system based on CNNs is proposed. Generally, a convolutional network usually has a large number of parameters which need millions of data and a great deal of time to train. To solve this problem, the strategy of transfer learning is utilized in this paper. Besides, further improvement is implemented on the chosen model to improve the performance of the network by changing some fully connected layers into convolutional connection. This is because that the weight shared feature of convolutional layers is able to reduce the number of parameters contained in a network. In addition, these convolutional kernels are decomposed into multi-layer and smaller convolutional kernels to get a better performance. Finally, the performance of the final optimized network is compared with unoptimized networks. Experimental results demonstrate that the final optimized network presents the best performance. © 2017, Springer International Publishing AG.
ISSN号：
0302-9743
是否译文：
否
发表时间：
2017-01-01
合写作者：
Song, Jiarong,Zhang, Tianyi,Han, Jiaming
通讯作者：
杨忠
发表时间：
2017-01-01
学位：工学博士学位
职称：教授
所在单位：自动化学院