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

页码：269 / 279

页数：11

共 50 条

[41] Content-based Image Retrieval using Deep Convolution Neural Network
Pukale, D. D.
Bhirud, S. G.
Katkar, V. D.
2017 INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION, CONTROL AND AUTOMATION (ICCUBEA), 2017,
[42] Breast Cancer Recognition Algorithm Based on Convolution Neural Network
Jiang, Jiafu
Li, Xinpei
Wang, Jin
Lim, Se-Jung
JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 2021, 11 (03) : 672 - 680
[43] Human Motion Recognition Based on Packet Convolution Neural Network
Ding, Yupeng
Li, Hongjun
Li, Zhengyu
2017 12TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND KNOWLEDGE ENGINEERING (IEEE ISKE), 2017,
[44] Improved Method for Estimating Number of People Based on Convolution Neural Network
Zhang Hongying
Wang Sainan
Hu Wenbo
LASER & OPTOELECTRONICS PROGRESS, 2018, 55 (12)
[45] Review of Deep Convolution Neural Network in Image Classification
Al-Saffar, Ahmed Ali Mohammed
Tao, Hai
Talab, Mohammed Ahmed
2017 INTERNATIONAL CONFERENCE ON RADAR, ANTENNA, MICROWAVE, ELECTRONICS, AND TELECOMMUNICATIONS (ICRAMET), 2017, : 26 - 31
[46] Detection of a casting defect tracked by deep convolution neural network
Jinhua Lin
Yu Yao
Lin Ma
Yanjie Wang
The International Journal of Advanced Manufacturing Technology, 2018, 97 : 573 - 581
[47] Recognition Method Of Non-Stationary Mechanical Vibration Signal Based On Convolution Neural Network
Li, Meixuan
2020 5TH INTERNATIONAL CONFERENCE ON SMART GRID AND ELECTRICAL AUTOMATION (ICSGEA 2020), 2020, : 217 - 221
[48] Image defogging algorithms based on multiscale convolution neural network
Chen Qing-jiang
Zhang Xue
Chai Yu-zhou
CHINESE JOURNAL OF LIQUID CRYSTALS AND DISPLAYS, 2019, 34 (02) : 220 - 227
[49] Research on traffic sign recognition method based on multi-scale convolution neural network
Wei T.
Chen X.
Yin Y.
Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University, 2021, 39 (04): : 891 - 900
[50] Ultrasonic detection of white etching defect based on convolution neural network*
Zhu Qi
Xu Duo
Zhang Yuan-Jun
Li Yu-Juan
Wang Wen
Zhang Hai-Yan
ACTA PHYSICA SINICA, 2022, 71 (24)

← 1 2 3 4 5 →