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Recognition of Banknote Fitness Based on a Fuzzy System Using Visible Light Reflection and Near-infrared Light Transmission Images

1
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea
2
Kisan Electronics, Sungsoo 2-ga 3-dong, Sungdong-gu, Seoul 133-831, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Sensors 2016, 16(6), 863; https://doi.org/10.3390/s16060863
Received: 4 April 2016 / Revised: 30 May 2016 / Accepted: 7 June 2016 / Published: 11 June 2016
(This article belongs to the Section Physical Sensors)
Fitness classification is a technique to assess the quality of banknotes in order to determine whether they are usable. Banknote classification techniques are useful in preventing problems that arise from the circulation of substandard banknotes (such as recognition failures, or bill jams in automated teller machines (ATMs) or bank counting machines). By and large, fitness classification continues to be carried out by humans, and this can cause the problem of varying fitness classifications for the same bill by different evaluators, and requires a lot of time. To address these problems, this study proposes a fuzzy system-based method that can reduce the processing time needed for fitness classification, and can determine the fitness of banknotes through an objective, systematic method rather than subjective judgment. Our algorithm was an implementation to actual banknote counting machine. Based on the results of tests on 3856 banknotes in United States currency (USD), 3956 in Korean currency (KRW), and 2300 banknotes in Indian currency (INR) using visible light reflection (VR) and near-infrared light transmission (NIRT) imaging, the proposed method was found to yield higher accuracy than prevalent banknote fitness classification methods. Moreover, it was confirmed that the proposed algorithm can operate in real time, not only in a normal PC environment, but also in an embedded system environment of a banknote counting machine. View Full-Text
Keywords: fitness classification; contact image sensor; fuzzy system; USD; KRW; Indian rupee (INR) fitness classification; contact image sensor; fuzzy system; USD; KRW; Indian rupee (INR)
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MDPI and ACS Style

Kwon, S.Y.; Pham, T.D.; Park, K.R.; Jeong, D.S.; Yoon, S. Recognition of Banknote Fitness Based on a Fuzzy System Using Visible Light Reflection and Near-infrared Light Transmission Images. Sensors 2016, 16, 863. https://doi.org/10.3390/s16060863

AMA Style

Kwon SY, Pham TD, Park KR, Jeong DS, Yoon S. Recognition of Banknote Fitness Based on a Fuzzy System Using Visible Light Reflection and Near-infrared Light Transmission Images. Sensors. 2016; 16(6):863. https://doi.org/10.3390/s16060863

Chicago/Turabian Style

Kwon, Seung Yong, Tuyen Danh Pham, Kang Ryoung Park, Dae Sik Jeong, and Sungsoo Yoon. 2016. "Recognition of Banknote Fitness Based on a Fuzzy System Using Visible Light Reflection and Near-infrared Light Transmission Images" Sensors 16, no. 6: 863. https://doi.org/10.3390/s16060863

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