期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2021
卷号：48
期号：4
语种：English
出版社：IAENG - International Association of Engineers
摘要：The dirty degree of banknotes determines to some extent whether banknotes can continue to circulate. This paper proposes a whale optimization algorithm based multi-layer support vector machine (WOA-MLSVMs) dirty degree recognition method based on the texture characteristics of banknote images. Based on the contact image sensor to collect the double-sided reflection images of the banknotes under red, green, blue, infrared and ultraviolet light, as well as the transmission images under the green light and infrared light, 22 texture characteristic parameters of the banknotes image based on the gray-scale co-occurrence matrix (GLCM) are extracted to describe the visual characteristics of the banknotes dirty degree, such as energy, entropy and inertia, etc. The banknotes images are selected based on the dirty degree recognition results of MLSVMs to establish the full-spectrum banknote dirty degree recognition sample data set. Five essential dimension estimation methods and seventeen data dimension reduction methods are combined to determine the essential dimension and the optimal dimension reduction method. Finally, WOA-MLSVMs realizes the full-spectrum banknote dirty degree recognition and the simulation results show the effectiveness of the proposed strategy.
