Research progress on visual image detection based on convolutional neural network

被引:0
作者
Lan J. [1 ]
Wang D. [1 ]
Shen X. [1 ]
机构
[1] School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing
来源
Lan, Jinhui (lanjh@ustb.edu.cn) | 1600年 / Science Press卷 / 41期
关键词
Computer vision; Convolutional neural network (CNN); Deep learning; Image detection;
D O I
10.19650/j.cnki.cjsi.J2006003
中图分类号
学科分类号
摘要
Visual image detection has great research significance and application value in the computer vision field. In recent years, the development of convolutional neural network (CNN) has led to the progress of visual image detection. A large number of new theories and new methods are applied to convolutional neural network, which improves the network feature expression ability, reduces the network complexity and improves the network performance. This paper presents the basic structure of Convolutional CNN, summarizes the improvements of CNN in recent years on different aspects, including convolutional layer, pooling layer, activation function, network regularization and network optimization, sorts various applications of CNN in visual image detection field and summarizes the advantages of CNN in visual image detection field, finally, prospects the future research direction. © 2020, Science Press. All right reserved.
引用
收藏
页码:167 / 182
页数:15
相关论文
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