The Analysis System of Counterfeit Banknote by Photo on Smartphone using Deep Learning Technique
Article Sidebar
Main Article Content
Abstract
The aims of this research is to develop system used for classifying of counterfeit banknote using image processing with deep learning technology to help the analysis of counterfeit banknotes for use on smartphones. The operation consisted of 5 steps: 1) Collecting 10 samples of datagroup, 100 images per group, a total of 1,000 images.2) Develop classification modeling using deep convolutional neural network technique with MobileNet algorithms through the TensorFlow library, train our model for 500 rounds. 3) Model evaluation. 4) Development of a user interface as a smartphone application. 5) System testing. The results of the experiment showed that a convolutional neural network model capable of accurately recognizing counterfeit banknotes with accuracy of 98.00%. The results of the application performance test. It was found that the application was able to analyze counterfeit banknotes using smartphone camera images and was able to recognize the images of all 10 types of banknotes with an average accuracy of 80.00%. In conclusion, this application can useful in detect of counterfeit banknotes.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
I/we certify that I/we have participated sufficiently in the intellectual content, conception and design of this work or the analysis and interpretation of the data (when applicable), as well as the writing of the manuscript, to take public responsibility for it and have agreed to have my/our name listed as a contributor. I/we believe the manuscript represents valid work. Neither this manuscript nor one with substantially similar content under my/our authorship has been published or is being considered for publication elsewhere, except as described in the covering letter. I/we certify that all the data collected during the study is presented in this manuscript and no data from the study has been or will be published separately. I/we attest that, if requested by the editors, I/we will provide the data/information or will cooperate fully in obtaining and providing the data/information on which the manuscript is based, for examination by the editors or their assignees. Financial interests, direct or indirect, that exist or may be perceived to exist for individual contributors in connection with the content of this paper have been disclosed in the cover letter. Sources of outside support of the project are named in the cover letter.
I/We hereby transfer(s), assign(s), or otherwise convey(s) all copyright ownership, including any and all rights incidental thereto, exclusively to the Journal, in the event that such work is published by the Journal. The Journal shall own the work, including 1) copyright; 2) the right to grant permission to republish the article in whole or in part, with or without fee; 3) the right to produce preprints or reprints and translate into languages other than English for sale or free distribution; and 4) the right to republish the work in a collection of articles in any other mechanical or electronic format.
We give the rights to the corresponding author to make necessary changes as per the request of the journal, do the rest of the correspondence on our behalf and he/she will act as the guarantor for the manuscript on our behalf.
All persons who have made substantial contributions to the work reported in the manuscript, but who are not contributors, are named in the Acknowledgment and have given me/us their written permission to be named. If I/we do not include an Acknowledgment that means I/we have not received substantial contributions from non-contributors and no contributor has been omitted.
References
Bank of Thailand, “Banknote History”, [Online], Available: https://www.bot.or.th/Thai/Banknotes/ Banknotes/HistoryAndSeriesOfBanknotes/Pages/All_Series_of_Banknotes.aspx. [Accessed: Sep. 8, 2019]
Sangkhit Chanthanapho, “From Shellfish to Paper Money,” Sparkling Diamond Publisher, Bangkok, 2016.
Nubbank.com, “How to Detect Counterfeit Banknotes”, [Online], Available: https://www.nubbank.com/ knowledge/Howtodetectcounterfeitbanknotes.html. [Accessed: Oct. 11, 2019]
R. Ye, C. Pan, M. Chang and Q. Yu, “Intelligent Defect Classification System based on Deep Learning,” Advances in Mechanical Engineering, Vol. 10, No. 3pp. 1–7, 2018.
T. Guo, J. Dong, H. Li and Y. Gao, "Simple Convolutional Neural Network on Image Classification", IEEE International Conference on Big Data Analysis, China, 2017, pp. 721-724.
F. Ertam and G. Aydın, "Data Classification with Deep Learning using Tensorflow," International Conference on Computer Science and Engineering, Antalya, pp. 755-758, 2017.
D. Wu, Y. Zhang, X. Jia, L. Tian, T. Li, L. Sui, D. Xie, and Y. Shan, “A High-performance CNN Processor based on FPGA for Mobilenets,” International Conference on Field Programmable Logic and Applications, pp. 136–143, 2019.
A. Verma, P. Singh and J. Alex, “Modified Convolution Neural Network Architecture Analysis for Facial Emotion Recognition,” International Conference on Systems, Signals and Image Processing, Croatia, pp. 169-173, 2019.
L. Soimart and P. Mookdarsanit, “Ingredients Estimation and Recommendation of Thai-foods,” SNRU Journal of Science and Technology, Vol.9, No.2, May-August. pp. 509-520, 2017.
J. Sanuksan and O. Surinta, “Deep Convolutional Neural Networks for Plant Recognition in the Natural Environment,” Journal of Science and Technology Mahasarakham University, Vol.38, No.2pp. 113-124, 2019.
S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, D. Stefanovic, “Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification,” Computational Intelligence and Neuroscience pp. 1–11, 2016.
M. Islam, N. Tasnim and S. Shuvo, “MobileNet Model for Classifying Local Birds of Bangladesh from Image Content using Convolutional Neural Network,” International Conference on Computing, Commuication and Network Technologies, India, pp.1-4, 2019.
M. Uddin, P. Das and M. Roney, “Image-based Approach for the Detection of Counterfeit Banknotes of Bangladesh,” IEEE 5thInternational Conference Informatics Electronics and Vision, Bangladesh, pp.1067-1072, 2016.
S. Yammen, N. Pimkhamwong and C. Rityen, “Classification of White Rice Seed by Image Proceesing,” RMUTP Research Journal, Vol.10, No.1pp. 1-14, 2016.
Y. Gulatee, P. Pongthano and C. Sakunrasrisuay, “Smartphone Application for Diabetes Behavior Study in Thailand,” Sripatun Review of Science and Technology, Vol.11, January-December, pp. 7-22, 2019.