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- Title
Web-based Implementation and Performance Evaluation of Mixed Foreign Coins Identification Using YOLO Object Detection.
- Authors
Fanzury, Nanda; Wahyutama, Aria Bisma; Mintae Hwang; Hoon Lee
- Abstract
Travelers who lack extensive experience or those visiting multiple countries in a short timeframe often need help to differentiate and identify the values of mixed foreign coins. This paper introduces a model that employs You Only Look Once (YOLO) object detection to identify various foreign coins within a single image frame and thoroughly evaluate its performance. The model, developed on Google Colab, has been trained using a dataset of 8,100 images and successfully identifies 18 different classes of coins, including the US dollar, European euro, and Chinese yuan. Implementing the model as a mobile-friendly web application allows users to upload images of their coins, and the application will provide identification results along with the option to receive the latest currency exchange information. The performance metrics scores of the model were 0.892, 0.932, and 0.925 for Precision, Recall, and Accuracy, respectively, showing satisfactory results.
- Subjects
FOREIGN exchange; COINS; RENMINBI; U.S. dollar; WEB-based user interfaces; CRYPTOCURRENCIES; IDENTIFICATION
- Publication
Journal of the Korea Institute of Information & Communication Engineering, 2024, Vol 28, Issue 6, p755
- ISSN
2234-4772
- Publication type
Article
- DOI
10.6109/jkiice.2024.28.6.755