Identification of Mixed Foreign Banknotes Using YOLOv8 Object Detection
Nanda Fanzury, Mintae Hwang · Journal of information and communication convergence engineering · 2025
This paper presents a novel approach for identifying banknotes using an object detection algorithm to help tourists, particularly the elderly and visually impaired.Traditional banknote identification methods require specialized equipment.Thus, this work adopts an image-based method to provide a portable solution for enhancing convenience and accessibility.Furthermore, this paper compares two object detection algorithms, YOLOv8 and Faster R-CNN, to determine the optimal algorithm for banknote identification.The comparison results show that YOLOv8 achieves a mean average precision (mAP) of 0.973 at an intersection over union (IoU) threshold of 0.5, outperforming Faster R-CNN and previous methods by over 14%.The proposed system can identify the two most used currencies globally, the US Dollar and European Euro, demonstrating high accuracy and robustness in identifying multiple banknotes with mixed currencies within a single image.The trained model is implemented in a web application that is accessible everywhere, making the system portable.