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

[1] Research on Image Recognition Technology Based on Convolution Neural Network
Wang Jinghe
[J]. 2019 4TH INTERNATIONAL WORKSHOP ON MATERIALS ENGINEERING AND COMPUTER SCIENCES (IWMECS 2019), 2019, : 147 - 151
[2] Research on fingerprint image recognition based on convolution neural network
Tian, Lifang
Xu, Huijuan
Zheng, Xin
[J]. INTERNATIONAL JOURNAL OF BIOMETRICS, 2021, 13 (01) : 64 - 79
[3] Deep convolution neural network for image recognition
Traore, Boukaye Boubacar
Kamsu-Foguem, Bernard
Tangara, Fana
[J]. ECOLOGICAL INFORMATICS, 2018, 48 : 257 - 268
[4] A Method of Ultrasonic Image Recognition for Thyroid Papillary Carcinoma Based on Deep Convolution Neural Network
Wang, Yonghua
Ke, Wei
Wan, Pin
[J]. NEUROQUANTOLOGY, 2018, 16 (05) : 757 - 768
[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
[J]. NEURAL INFORMATION PROCESSING (ICONIP 2017), PT II, 2017, 10635 : 82 - 91
[6] Power Defect Recognition Method Based on Fixed-point Adaptive Selection Convolution Neural Network
Dai, Yongdong
Yao, Jianguang
Li, Yong
Mao, Feng
Wen, Zhike
Cao, Shipeng
[J]. Gaodianya Jishu/High Voltage Engineering, 2021, 47 (11): : 3827 - 3835
[7] New and Old Banknote Recognition Based on Convolutional Neural Network
Liu, Yongjiao
He, Jianbiao
Li, Min
[J]. ICOMS 2018: 2018 INTERNATIONAL CONFERENCE ON MATHEMATICS AND STATISTICS, 2018, : 92 - 97
[8] Sonar Image Target Detection and Recognition Based on Convolution Neural Network
Wu Yanchen
[J]. MOBILE INFORMATION SYSTEMS, 2021, 2021
[9] Korean Sign Language Recognition Based on Image and Convolution Neural Network
Shin, Hyojoo
Kim, Woo Je
Jang, Kyoung-ae
[J]. ICIGP 2019: PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON IMAGE AND GRAPHICS PROCESSING / 2019 5TH INTERNATIONAL CONFERENCE ON VIRTUAL REALITY, 2019, : 52 - 55
[10] Image Recognition Based on Convolution Neural Network with Small Data Set
Chen, Lan
Jiang, Zhiyuan
Wang, Zhiyong
[J]. 2019 PHOTONICS & ELECTROMAGNETICS RESEARCH SYMPOSIUM - FALL (PIERS - FALL), 2019, : 816 - 820

← 1 2 3 4 5 →