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

Gulcin Baykal, Ugür Demir, Ira Shyti, Gözde Ünal

Yapay Zeka ve Veri Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

3 Atıf (Scopus)

Özet

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.

Tercüme edilen katkı başlığı	Turkish lira banknotes classification using deep convolutional neural networks
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	1-4
Sayfa sayısı	4
ISBN (Elektronik)	9781538615010
DOI'lar	https://doi.org/10.1109/SIU.2018.8404606
Yayın durumu	Yayınlandı - 5 Tem 2018
Etkinlik	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Turkey Süre: 2 May 2018 → 5 May 2018

Yayın serisi

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

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Ülke/Bölge	Turkey
Şehir	Izmir
Periyot	2/05/18 → 5/05/18

Bibliyografik not

Publisher Copyright:
© 2018 IEEE.

Keywords

Bank note classification
Convolutionan neural network
Deep learning

Belgeye Erişim

10.1109/SIU.2018.8404606

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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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title = "Turk lirasi banknotlarinin derin evri{\c s}imsel sinir aǧlari ile siniflandirilmasi",

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.",

keywords = "Bank note classification, Convolutionan neural network, Deep learning",

author = "Gulcin Baykal and Ug{\"u}r Demir and Ira Shyti and G{\"o}zde {\"U}nal",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 ; Conference date: 02-05-2018 Through 05-05-2018",

year = "2018",

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doi = "10.1109/SIU.2018.8404606",

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series = "26th IEEE Signal Processing and Communications Applications Conference, SIU 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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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).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Turk lirasi banknotlarinin derin evrişimsel sinir aǧlari ile siniflandirilmasi

AU - Baykal, Gulcin

AU - Demir, Ugür

AU - Shyti, Ira

AU - Ünal, Gözde

PY - 2018/7/5

Y1 - 2018/7/5

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.

KW - Bank note classification

KW - Convolutionan neural network

KW - Deep learning

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DO - 10.1109/SIU.2018.8404606

M3 - Konferans katkısı

AN - SCOPUS:85050829632

T3 - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

SP - 1

EP - 4

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

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 2 May 2018 through 5 May 2018

ER -

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

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Keywords

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap