Development of an Automated Coin Grading System: Integrating Image Preprocessing, Feature Extraction, and ML Modeling
| dc.contributor.author | Chen, Jianzhu | en |
| dc.contributor.committeechair | Jones, Creed Farris | en |
| dc.contributor.committeemember | Lester, Luke F. | en |
| dc.contributor.committeemember | Abbott, Amos L. | en |
| dc.contributor.department | Electrical and Computer Engineering | en |
| dc.date.accessioned | 2024-12-21T09:00:24Z | en |
| dc.date.available | 2024-12-21T09:00:24Z | en |
| dc.date.issued | 2024-12-20 | en |
| dc.description.abstract | For more than 70 years, the Sheldon Coin Grading Scale has been essential in quantifying the value of coins within the coin collecting industry. Traditionally, coin grading has relied on human graders who may deliver inconsistent results. This inconsistency leads to variations in coin values. In this thesis, we present an automated coin grading system that uses image preprocessing, feature extraction, and advanced machine learning techniques to predict the grade across different coin types. Our system employs synthetic reference masks to identify "expected" regions, like the contours of reliefs, and "unexpected" regions, such as surface non-uniformities. All detected significant elements and tiny elements, extracted from these regions, will serve as one of the feature sets. Additionally, we extract color histograms as another feature set to analyze color and texture in detail. Both feature sets from the obverse and reverse sides of the coins are processed using a multi-layer perceptron (MLP) model and a random forest model. The best-performing model is then selected to grade the coins by analyzing their overall wear patterns and color characteristics. Our grading system has demonstrated an accuracy of up to 91.3% in predicting the Sheldon Grading Scale across five coin types, allowing for a grading tolerance of ±4. For a single coin type (Franklin Half Dollar), it has achieved an accuracy of up to 95.1% with a tolerance of ±1. | en |
| dc.description.abstractgeneral | For over 70 years, the Sheldon Coin Grading Scale has been crucial for quantifying the value of coins in the large coin collecting industry. Traditionally, coin grading has depended on human graders, which may lead to inconsistent results and variations in coin values. In this thesis, we present an automated coin grading system that uses advanced image processing and advanced machine learning techniques to predict the grade of various coin types. Our system uses synthetic reference masks to identify key areas, such as the contours of the designs on the coins, and detect any irregularities on the surface. We analyze significant details and tiny elements from these areas to form one set of features. Additionally, we extract color information to examine the coin's color and texture. Both sets of features, from the front and back of the coins, are processed using a multi-layer perceptron (MLP) model and a random forest model, which grades the coins by assessing their overall wear and color. Our grading system has demonstrated an accuracy of up to 91.3% in predicting the Sheldon Grading Scale across five coin types, allowing for a grading tolerance of ±4. For a single coin type (Franklin Half Dollar), it has achieved an accuracy of up to 95.1% with a tolerance of ±1. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:41976 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/123863 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | Automated Coin Grading | en |
| dc.subject | Machine Learning | en |
| dc.subject | Computer Vision | en |
| dc.subject | and multi-layer perceptron | en |
| dc.title | Development of an Automated Coin Grading System: Integrating Image Preprocessing, Feature Extraction, and ML Modeling | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Computer Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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