CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES

Yekta Said Can

doi:10.17482/uumfd.1054013

TR EN

Yapay Sinir Ağları ve Destek Vektör Makineleri Kullanılarak Gerçek ve Sahte Altın Sınıflandırılması

Abstract

Altın, en çok taklit edilen değerli metallerden biridir. Bakırın rengi altına benzer. Bu nedenle bakır, renk sahteciliği için en yaygın kullanılan malzemelerden biridir. Kimyasal özellikler söz konusu olduğunda, volfram altına benzer (altın ve tungstenin yoğunluğu sırasıyla 19.30 g/ml ve 19.25 g/ml'dir), bu nedenle kimyasal bir sahte olarak kullanılabilir. Altının saflığı X-ray ile belirlenebilir, ancak bu yöntem maliyetlidir. Bu yazıda, sahte altın tespiti için kuyumcuların mevcut düşük maliyetli yöntemleri ve sahte parayı tespit etmek için kullanılan düşük maliyetli yöntemler denenmiştir. Bir yüzeye altın bir madde çarptığında, ses frekansı aynı deney bakır ile yapıldığındaki sesin frekansından daha yüksektir. Ayrıca, sahte altın rengi gerçek olanlardan daha parlaktır. Altın rengi benzersizdir ve "altın sarısı" olarak adlandırılır. Bu araştırmada ses ve görüntü işleme yöntemleri kullanılarak sahte ve orijinal altın ayrımı yapılmıştır. Görüntü işleme kısmı için, önce görüntüden altını segmentlere ayırmak için CNN tabanlı bir araç kutusu uygulanır. Bundan sonra, altın ve bakır malzemelerin rengini ayırt etmek için derin Evrişimli Sinir Ağları kullanılır. Hem ses hem de görüntü işleme teknikleri ile umut verici sonuçlar elde edilmektedir.

Keywords

CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES

Abstract

Gold is one of the most counterfeited precious metals. The color of copper is like gold. For this reason, copper is one of the most used materials for color counterfeiting. When the chemical properties are concerned, wolfram is like gold (density of gold and tungsten are 19.30 g/ml and 19.25 g/ml, respectively), so it can be used as a chemical counterfeit. The purity of gold can be determined by X-ray, but this method is costly. The current low-cost methods of jewelers have been experimented with for counterfeit gold detection in this paper. When a gold matter is hit by a subject, the sound frequency is higher than the frequency of sound when the same experiment is done with copper. Furthermore, counterfeit gold color is brighter than real ones. The color of gold is unique, and it is called "gold yellow". In this research, by employing sound and image processing, counterfeit and original gold are differentiated. For the image processing part, first a Convolutional Neural Network (CNN)-based toolbox for segmenting the gold material is applied. Then, deep CNNs for differentiating the color of the gold and copper materials are employed. Promising results are achieved with both sound and image processing techniques.

Keywords

Supporting Institution

Tübitak

Project Number

TEYDEB 9150222 Akıllı Altın Tanıma Teknolojisi

Thanks

This study was carried out as a part of the E!9874 Smart Magnetic Identification Technology (TEYDEB 9150222 Smart Gold Identification Technology) project within the EU Eurostars Programme. I would like to thank the KuveytTurk Participation Bank RD Center team for their contribution to the project. I would like to also thank Professor Fatih ALAGÖZ for his contributions in this study and for managing the project.

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Details

Primary Language

English

Subjects

Artificial Intelligence, Software Engineering, Computer Software

Journal Section

Research Article

Authors

Yekta Said Can ^*
0000-0002-6614-0183
Türkiye

Publication Date

April 30, 2022

Submission Date

January 5, 2022

Acceptance Date

February 27, 2022

Published in Issue

Year 2022 Volume: 27 Number: 1

DOI

https://doi.org/10.17482/uumfd.1054013

IZ

https://izlik.org/JA96EL73ZP

Cite

RIS / Bibtex

APA

Can, Y. S. (2022). CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 27(1), 89-102. https://doi.org/10.17482/uumfd.1054013

AMA

1.Can YS. CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES. UUJFE. 2022;27(1):89-102. doi:10.17482/uumfd.1054013

Chicago

Can, Yekta Said. 2022. “CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 27 (1): 89-102. https://doi.org/10.17482/uumfd.1054013.

EndNote

Can YS (April 1, 2022) CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 27 1 89–102.

IEEE

[1]Y. S. Can, “CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES”, UUJFE, vol. 27, no. 1, pp. 89–102, Apr. 2022, doi: 10.17482/uumfd.1054013.

ISNAD

Can, Yekta Said. “CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 27/1 (April 1, 2022): 89-102. https://doi.org/10.17482/uumfd.1054013.

JAMA

1.Can YS. CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES. UUJFE. 2022;27:89–102.

MLA

Can, Yekta Said. “CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, vol. 27, no. 1, Apr. 2022, pp. 89-102, doi:10.17482/uumfd.1054013.

Vancouver

1.Yekta Said Can. CLASSIFICATION OF ORIGINAL AND COUNTERFEIT GOLD MATTERS BY APPLYING DEEP NEURAL NETWORKS AND SUPPORT VECTOR MACHINES. UUJFE. 2022 Apr. 1;27(1):89-102. doi:10.17482/uumfd.1054013