Banknote Image Defect Recognition Method Based on Convolution Neural Network

被引：10
|
作者：
Wang Ke [1 ,2 ]
Wang Huiqin [1 ,2 ]
Shu Yue [3 ]
Mao Li [2 ]
Qiu Fengyan [4 ,5 ]
机构：
[1] Xian Univ Architecture & Technol, Sch Management, Xian 710055, Peoples R China
[2] Xian Univ Architecture & Technol, Sch Informat & Control Engineer, Xian 710055, Peoples R China
[3] Chengdu Banknote Printing Ltd, Chengdu 611103, Peoples R China
[4] Peoples Bank China, Business Dept, Xian Branch, Xian 710002, Peoples R China
[5] Peoples Bank China, Dept Management, Xian Branch, Xian 710002, Peoples R China
来源：
INTERNATIONAL JOURNAL OF SECURITY AND ITS APPLICATIONS | 2016年 / 10卷 / 06期
关键词：
Convolution Neural Network; Defect Recognition; Banknote Image; Deep-learning;
D O I：
10.14257/ijsia.2016.10.6.26
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
There are shortcomings in the currently used traditional CCD imaging system which can automatically recognize banknote image defect, such as the need to manually extract the defect characteristics and low accuracy rate of detection results. This paper briefly introduced the advantage of convolution Neural Network (CNN) in image classification and designed a image defect identification method based on convolutional neural network (CNN). The experimental results on data sets show that the identification accuracy rate of this method is 95.6%, which is significantly better than traditional identification method.
引用
收藏
页码：269 / 279
页数：11
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