Brazilian coin counter research report

Yian Wu; Xiran Lin; Xie Wu; Han Cheng

doi:10.54254/2755-2721/14/20230795

Research Article

Open access

Published on 23 October 2023

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Wu,Y.;Lin,X.;Wu,X.;Cheng,H. (2023). Brazilian coin counter research report. Applied and Computational Engineering,14,235-243.

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Brazilian coin counter research report

Yian Wu *^,1, Xiran Lin ², Xie Wu ³, Han Cheng ⁴

¹ The Study
² Beijing21st Century International School
³ The Pennsylvania State University
⁴ Tsinglan Schoo

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/14/20230795

Abstract

Though coins are less used in the informational era, a significant amount of the population still uses physical coins for financial transactions. Also, when the country’s government collects the coins for equivalent digital currency transactions, coin identification is still vital yet tedious. Moreover, the dated coins and the difference in illuminations. This novel presents a coin identification and validation model based on the Alex Net convolutional model. It identifies the value of a coin through the numbers, animals, and plants on the two sides. The model employs data enhancement, feature attention layers, max pooling, and residue groups. We collected 2826 images of Brazilian coins with reverse motifs, and the experimental accuracy of our model reached 0.97. The code part has shown here: https://github.com/Erik-Xie/-Brazilian-Coin-counter-Research-Report.git

Keywords

coin validation and identification, Alex Net convolutional network

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References

[1]. Dabic, S. (2011, September 7). WO2012036956A1 - Coin Identification Method and apparatus. Google Patents. Retrieved March 3, 2023, fromhttps://patents.google.com/patent/WO2012036956A1/en

[2]. Pham, T.D. et al. (2022) Deep learning-based detection of fake multinational banknotes in a cross-dataset environment utilizing smartphone cameras for assisting visually impaired individuals, MDPI. Multidisciplinary Digital Publishing Institute. Available at: https://www.mdpi.com/2227-7390/10/9/1616 (Accessed: March 10, 2023).

[3]. C. Park, S. W. Cho, N. R. Baek, J. Choi and K. R. Park, "Deep Feature-Based Three-Stage Detection of Banknotes and Coins for Assisting Visually Impaired People," in IEEE Access, vol. 8, pp. 184598-184613, 2020, doi: 10.1109/ACCESS.2020.3029526.

[4]. Yufeng, X., & Yan, W. Q. (2021). Fast‐moving coin recognition using deep learning. Multimedia Tools and Applications, 80(16), 24111-24120. https://doi.org/10.1007/s11042-021-10857-5

[5]. https://www.kaggle.com/datasets/lgmoneda/br-coins

Cite this article

Wu,Y.;Lin,X.;Wu,X.;Cheng,H. (2023). Brazilian coin counter research report. Applied and Computational Engineering,14,235-243.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

Conference website: https://2023.confcds.org/

ISBN：978-1-83558-019-6(Print) / 978-1-83558-020-2(Online)

Conference date: 14 July 2023

Editor：Alan Wang, Marwan Omar, Roman Bauer

Series: Applied and Computational Engineering

Volume number: Vol.14

ISSN：2755-2721(Print) / 2755-273X(Online)

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