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Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Recognizing Egyptian currency for people with visual impairment using deep learning models.

Ahmed M Ghanem^1,2, Hassan A Youness³, Mohamed Wahba⁴

¹Department of Computers & Systems Engineering, Faculty of Engineering, Minia University, Minya, 61519, Egypt. ahmed.addo.pg@eng.s-mu.edu.eg.

Scientific Reports

|October 1, 2025

Summary

This summary is machine-generated.

This study introduces a real-time Egyptian currency recognition system using advanced AI models to help visually impaired individuals manage money. YOLOv10 demonstrated superior performance, enhancing financial independence and accessibility.

Keywords:

Artificial intelligence Generalized efficient layer aggregation network (GELAN)Neural network Non-maximum suppression (NMS)YOLO

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Area of Science:

Computer Vision
Artificial Intelligence
Assistive Technology

Background:

Visually impaired individuals face challenges in independent financial transactions.
Existing currency recognition systems often lack accuracy for regional currencies.
There is a need for efficient and reliable assistive technology for financial inclusion.

Purpose of the Study:

To develop and evaluate a real-time Egyptian currency recognition system.
To enhance the financial independence and security of visually impaired users.
To compare the performance of YOLOv8, YOLOv9, and YOLOv10 for banknote identification.

Main Methods:

Utilized deep learning models: YOLOv8, YOLOv9, and YOLOv10.
Trained and evaluated models on a dataset of 2,000 annotated Egyptian banknote images.
Incorporated innovations like context aggregation, GELAN, and NMS-free training.

Main Results:

YOLOv10 achieved the highest performance metrics: 0.9678 precision, 0.9715 F1 score, and 0.9934 mAP@0.5.
The developed system demonstrated high accuracy and low latency in identifying Egyptian banknotes.
Performance surpassed both YOLOv8 and YOLOv9, as well as traditional methods.

Conclusions:

The novel AI system significantly improves Egyptian currency recognition for visually impaired users.
YOLOv10 offers a scalable and practical solution for accessible AI applications.
The system promotes financial inclusion and supports advancements in assistive technology.