BDCoins: A Comprehensive Dataset for Bangladeshi Coin Detection Using YOLOv11
Issued Date
2025-01-01
Resource Type
Scopus ID
2-s2.0-105041629442
Journal Title
2025 28th International Conference on Computer and Information Technology Iccit 2025
Start Page
1630
End Page
1635
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SCOPUS
Bibliographic Citation
2025 28th International Conference on Computer and Information Technology Iccit 2025 (2025) , 1630-1635
Suggested Citation
Iqbal K.N., Taj T.A., Mahee M.N.I., Fahim M., Zereen A.N. BDCoins: A Comprehensive Dataset for Bangladeshi Coin Detection Using YOLOv11. 2025 28th International Conference on Computer and Information Technology Iccit 2025 (2025) , 1630-1635. 1635. doi:10.1109/ICCIT68739.2025.11490358 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/117423
Title
BDCoins: A Comprehensive Dataset for Bangladeshi Coin Detection Using YOLOv11
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Author's Affiliation
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Abstract
Currency detection is a complex task due to the diverse patterns and rich features found in different currencies. Identifying coins presents unique challenges, as their appearance can vary with orientation and environmental conditions. Recent approaches in the field shift from manual feature engineering to automated systems using deep learning, which demonstrate superior accuracy and robustness. Object detection models like YOLO have become popular for coin recognition due to their speed and accuracy, with research works applying various versions to identify specific national currencies. Despite these advances, research on Bangladeshi currency detection, particularly for coins, remains very limited. A significant research gap exists because there is no large, publicly available dataset that includes the newly designed 1, 2, and 5 Taka coins, their variations, and images of both their front and back sides. This paper addresses this gap by introducing BDCoins, a custom benchmark dataset containing 11,133 annotated images of Bangladeshi coins. The dataset encompasses all old and new variations of the 1,2, and 5 Taka denominations, with images captured under diverse conditions to reflect real-world scenarios. A YOLOv11 model is trained and validated on this dataset for detection and classification. The model achieves an F1 score of 0.983 and demonstrates 0.982 accuracy in testing, providing a foundational tool for automated Bangladeshi currency recognition systems.