A feasibility study of deep neural networks for the recognition of banknotes regarding central bank requirements

Autor:	Schulte, Julia, Staps, Daniel, Lampe, Alexander
Rok vydání:	2019
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Electrical Engineering and Systems Science - Image and Video Processing Statistics - Machine Learning I.7.5 I.5.1 I.2.6 G.1.6 G.3
Druh dokumentu:	Working Paper
Popis:	This paper contains a feasibility study of deep neural networks for the classification of Euro banknotes with respect to requirements of central banks on the ATM and high speed sorting industry. Instead of concentrating on the accuracy for a large number of classes as in the famous ImageNet Challenge we focus thus on conditions with few classes and the requirement of rejection of images belonging clearly to neither of the trained classes (i.e. classification in a so-called 0-class). These special requirements are part of frameworks defined by central banks as the European Central Bank and are met by current ATMs and high speed sorting machines. We also consider training and classification time on state of the art GPU hardware. The study concentrates on the banknote recognition whereas banknote class dependent authenticity and fitness checks are a topic of its own which is not considered in this work. Comment: 6 pages, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1907.07890 Zobrazit plný text záznamu View this record from Arxiv