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
相关论文
共 50 条
  • [1] Research on fingerprint image recognition based on convolution neural network
    Tian, Lifang
    Xu, Huijuan
    Zheng, Xin
    INTERNATIONAL JOURNAL OF BIOMETRICS, 2021, 13 (01) : 64 - 79
  • [2] Research on Image Recognition Technology Based on Convolution Neural Network
    Wang Jinghe
    2019 4TH INTERNATIONAL WORKSHOP ON MATERIALS ENGINEERING AND COMPUTER SCIENCES (IWMECS 2019), 2019, : 147 - 151
  • [3] Satellite Image Matching Method Based on Deep Convolution Neural Network
    Fan D.
    Dong Y.
    Zhang Y.
    2018, SinoMaps Press (47): : 844 - 853
  • [4] Deep convolution neural network for image recognition
    Traore, Boukaye Boubacar
    Kamsu-Foguem, Bernard
    Tangara, Fana
    ECOLOGICAL INFORMATICS, 2018, 48 : 257 - 268
  • [5] An Ultrasonic Image Recognition Method for Papillary Thyroid Carcinoma Based on Depth Convolution Neural Network
    Ke, Wei
    Wang, Yonghua
    Wan, Pin
    Liu, Weiwei
    Li, Hailiang
    NEURAL INFORMATION PROCESSING (ICONIP 2017), PT II, 2017, 10635 : 82 - 91
  • [6] Korean Sign Language Recognition Based on Image and Convolution Neural Network
    Shin, Hyojoo
    Kim, Woo Je
    Jang, Kyoung-ae
    ICIGP 2019: PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON IMAGE AND GRAPHICS PROCESSING / 2019 5TH INTERNATIONAL CONFERENCE ON VIRTUAL REALITY, 2019, : 52 - 55
  • [7] A Face Recognition System Based on Convolution Neural Network
    Qiao, Shijie
    Ma, Jie
    2018 CHINESE AUTOMATION CONGRESS (CAC), 2018, : 1923 - 1927
  • [8] Facial Expression Recognition Method Based on Cascade Convolution Neural Network
    Liu, Weida
    Fang, Jian
    IWCMC 2021: 2021 17TH INTERNATIONAL WIRELESS COMMUNICATIONS & MOBILE COMPUTING CONFERENCE (IWCMC), 2021, : 1012 - 1015
  • [9] Convolution neural network SAR image target recognition based on transfer learning
    Chen Lifu
    Wu Hong
    Cui Xianliang
    Guo Zhenghua
    Jia Zhiwei
    CHINESE SPACE SCIENCE AND TECHNOLOGY, 2018, 38 (06) : 45 - 51
  • [10] Wood Microscopic Image Identification Method Based on Convolution Neural Network
    Zhao, Ziyu
    Yang, Xiaoxia
    Ge, Zhedong
    Guo, Hui
    Zhou, Yucheng
    BIORESOURCES, 2021, 16 (03): : 4986 - 4999
12345