Euro Coin Recognition using YOLOv7

-, Syed Ahmed Omer (2023) Euro Coin Recognition using YOLOv7. Masters thesis, Dublin, National College of Ireland.

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Abstract

Coins have long been a common medium of exchange for financial, charitable, and retail transactions. Even in the present day, when the majority of transactions take place online, coins remain a vital and trustworthy form of payment. Coins come in different denominations, and counting them is a laborious, error-prone process that takes a lot of time. Conventional coin-counting systems arrange and tally the coins according to their physical attributes, such as weight, size, and form, fake coins with similar physical features can easily fool the conventional system. In this paper, we propose an image-based counting system using the state-of-the-art deep learning object detection model YOLOv7, which is capable of simultaneously detecting multiple coins in an image, 99% of recall and precision was achieved by the YOLOv7 model. It has applications ranging from financial, retail, banking institutions, and charity trusts for coin recognition and counting.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Shahid, Abdul UNSPECIFIED
Uncontrolled Keywords:	coin recognition; YOLOv7; deep learning; object detection; coin counting
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HG Finance > Money > Currency H Social Sciences > HG Finance > Money > Currency > Euro (Single European Currency) H Social Sciences > HG Finance > Financial Services Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	29 Apr 2025 14:56
Last Modified:	06 May 2025 13:44
URI:	https://norma.ncirl.ie/id/eprint/7482

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