TY - JOUR
T1 - Deep Learning-Based Detection of Fake Multinational Banknotes in a Cross-Dataset Environment Utilizing Smartphone Cameras for Assisting Visually Impaired Individuals
AU - Pham, Tuyen Danh
AU - Lee, Young Won
AU - Park, Chanhum
AU - Park, Kang Ryoung
N1 - Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - The automatic handling of banknotes can be conducted not only by specialized facilities, such as vending machines, teller machines, and banknote counters, but also by handheld devices, such as smartphones, with the utilization of built-in cameras and detection algorithms. As smartphones are becoming increasingly popular, they can be used to assist visually impaired individuals in daily tasks, including banknote handling. Although previous studies regarding banknote detection by smartphone cameras for visually impaired individuals have been conducted, these studies are limited, even when conducted in a cross-dataset environment. Therefore, we propose a deep learning-based method for detecting fake multinational banknotes using smartphone cameras in a cross-dataset environment. Experimental results of the self-collected genuine and fake multinational datasets for US dollar, Euro, Korean won, and Jordanian dinar banknotes confirm that our method demonstrates a higher detection accuracy than conventional “you only look once, version 3” (YOLOv3) methods and the combined method of YOLOv3 and the state-of-the-art convolutional neural network (CNN).
AB - The automatic handling of banknotes can be conducted not only by specialized facilities, such as vending machines, teller machines, and banknote counters, but also by handheld devices, such as smartphones, with the utilization of built-in cameras and detection algorithms. As smartphones are becoming increasingly popular, they can be used to assist visually impaired individuals in daily tasks, including banknote handling. Although previous studies regarding banknote detection by smartphone cameras for visually impaired individuals have been conducted, these studies are limited, even when conducted in a cross-dataset environment. Therefore, we propose a deep learning-based method for detecting fake multinational banknotes using smartphone cameras in a cross-dataset environment. Experimental results of the self-collected genuine and fake multinational datasets for US dollar, Euro, Korean won, and Jordanian dinar banknotes confirm that our method demonstrates a higher detection accuracy than conventional “you only look once, version 3” (YOLOv3) methods and the combined method of YOLOv3 and the state-of-the-art convolutional neural network (CNN).
KW - cross-dataset environment
KW - deep learning
KW - multinational fake banknote detection
KW - smartphone camera
KW - visually impaired people
UR - http://www.scopus.com/inward/record.url?scp=85130266865&partnerID=8YFLogxK
U2 - 10.3390/math10091616
DO - 10.3390/math10091616
M3 - Article
AN - SCOPUS:85130266865
SN - 2227-7390
VL - 10
JO - Mathematics
JF - Mathematics
IS - 9
M1 - 1616
ER -