Turk lirasi banknotlarinin derin evrişimsel sinir aǧlari ile siniflandirilmasi

Gulcin Baykal; Ugür Demir; Ira Shyti; Gözde Ünal

doi:10.1109/SIU.2018.8404606

Turk lirasi banknotlarinin derin evrişimsel sinir aǧlari ile siniflandirilmasi

Translated title of the contribution: Turkish lira banknotes classification using deep convolutional neural networks

Gulcin Baykal, Ugür Demir, Ira Shyti, Gözde Ünal

Department of Artificial Intelligence and Data Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

While the technology improves rapidly in today's world, the fact that visually impaired people still face complications about monetary situations in their social life reveals technology is needed to propose a solution. In this study, a system to classify Turkish Lira banknotes is implemented with convolutional neural networks and the results of different architectures are compared. A new and unique dataset of Turkish Lira banknotes is prepared to train, evaluate and test the system. The state-of-the-art deep learning models are used with fine-tuning and as a result of comparison it is shown that DenseNet-121 architecture has achived 93,15% test accuracy on this dataset which is the best performance.

Translated title of the contribution	Turkish lira banknotes classification using deep convolutional neural networks
Original language	Turkish
Title of host publication	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781538615010
DOIs	https://doi.org/10.1109/SIU.2018.8404606
Publication status	Published - 5 Jul 2018
Event	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Turkey Duration: 2 May 2018 → 5 May 2018

Publication series

Name	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

Conference

Conference	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Country/Territory	Turkey
City	Izmir
Period	2/05/18 → 5/05/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Access to Document

10.1109/SIU.2018.8404606

Cite this

Baykal, G., Demir, U., Shyti, I., & Ünal, G. (2018). Turk lirasi banknotlarinin derin evrişimsel sinir aǧlari ile siniflandirilmasi. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 (pp. 1-4). (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2018.8404606

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abstract = "While the technology improves rapidly in today's world, the fact that visually impaired people still face complications about monetary situations in their social life reveals technology is needed to propose a solution. In this study, a system to classify Turkish Lira banknotes is implemented with convolutional neural networks and the results of different architectures are compared. A new and unique dataset of Turkish Lira banknotes is prepared to train, evaluate and test the system. The state-of-the-art deep learning models are used with fine-tuning and as a result of comparison it is shown that DenseNet-121 architecture has achived 93,15% test accuracy on this dataset which is the best performance.",

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Baykal, G, Demir, U, Shyti, I & Ünal, G 2018, Turk lirasi banknotlarinin derin evrişimsel sinir aǧlari ile siniflandirilmasi. in 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Izmir, Turkey, 2/05/18. https://doi.org/10.1109/SIU.2018.8404606

Turk lirasi banknotlarinin derin evrişimsel sinir aǧlari ile siniflandirilmasi. / Baykal, Gulcin; Demir, Ugür; Shyti, Ira et al.
26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-4 (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Ünal, Gözde

PY - 2018/7/5

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N2 - While the technology improves rapidly in today's world, the fact that visually impaired people still face complications about monetary situations in their social life reveals technology is needed to propose a solution. In this study, a system to classify Turkish Lira banknotes is implemented with convolutional neural networks and the results of different architectures are compared. A new and unique dataset of Turkish Lira banknotes is prepared to train, evaluate and test the system. The state-of-the-art deep learning models are used with fine-tuning and as a result of comparison it is shown that DenseNet-121 architecture has achived 93,15% test accuracy on this dataset which is the best performance.

AB - While the technology improves rapidly in today's world, the fact that visually impaired people still face complications about monetary situations in their social life reveals technology is needed to propose a solution. In this study, a system to classify Turkish Lira banknotes is implemented with convolutional neural networks and the results of different architectures are compared. A new and unique dataset of Turkish Lira banknotes is prepared to train, evaluate and test the system. The state-of-the-art deep learning models are used with fine-tuning and as a result of comparison it is shown that DenseNet-121 architecture has achived 93,15% test accuracy on this dataset which is the best performance.

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Baykal G, Demir U, Shyti I, Ünal G. Turk lirasi banknotlarinin derin evrişimsel sinir aǧlari ile siniflandirilmasi. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-4. (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018). doi: 10.1109/SIU.2018.8404606

Turk lirasi banknotlarinin derin evrişimsel sinir aǧlari ile siniflandirilmasi

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