Recognizing Banknote Fitness with a Visible Light One Dimensional Line Image Sensor

Sensors

Tuyen Danh Pham¹  Young Ho Park¹  Seung Yong Kwon¹  Dat Tien Nguyen¹  Husan Vokhidov¹  Kang Ryoung Park¹  Dae Sik Jeong¹  Sungsoo Yoon² 

[1] Division of Electronics and Electrical Engineering, Dongguk University, 30, Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea; E-Mails:;Kisan Electronics, Sungsoo 2-ga 3-dong, Sungdong-gu, Seoul 133-831, Korea; E-Mail:

关键词: classification of banknote fitness;    one-dimensional line image sensor of visible light;    discrete wavelet transform;    linear regression analysis;    support vector machine;   

DOI  :  10.3390/s150921016

来源: mdpi

【 摘 要 】

In general, dirty banknotes that have creases or soiled surfaces should be replaced by new banknotes, whereas clean banknotes should be recirculated. Therefore, the accurate classification of banknote fitness when sorting paper currency is an important and challenging task. Most previous research has focused on sensors that used visible, infrared, and ultraviolet light. Furthermore, there was little previous research on the fitness classification for Indian paper currency. Therefore, we propose a new method for classifying the fitness of Indian banknotes, with a one-dimensional line image sensor that uses only visible light. The fitness of banknotes is usually determined by various factors such as soiling, creases, and tears, etc. although we just consider banknote soiling in our research. This research is novel in the following four ways: first, there has been little research conducted on fitness classification for the Indian Rupee using visible-light images. Second, the classification is conducted based on the features extracted from the regions of interest (ROIs), which contain little texture. Third, 1-level discrete wavelet transformation (DWT) is used to extract the features for discriminating between fit and unfit banknotes. Fourth, the optimal DWT features that represent the fitness and unfitness of banknotes are selected based on linear regression analysis with ground-truth data measured by densitometer. In addition, the selected features are used as the inputs to a support vector machine (SVM) for the final classification of banknote fitness. Experimental results showed that our method outperforms other methods.

【 授权许可】

CC BY   
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