Real-Time Currency Detection and Voice Feedback for Visually Impaired Individuals

TL;DR

This paper presents a real-time currency detection system using YOLOv8 nano and voice feedback to assist visually impaired individuals in identifying multiple currencies with high accuracy.

Contribution

It introduces a novel deep learning model with a custom detection head for accurate, real-time currency recognition tailored for visually impaired users.

Findings

Achieved 97.73% detection accuracy

High recall and F1-score for currency detection

Effective voice feedback implementation

Abstract

Technologies like smartphones have become an essential in our daily lives. It has made accessible to everyone including visually impaired individuals. With the use of smartphone cameras, image capturing and processing have become more convenient. With the use of smartphones and machine learning, the life of visually impaired can be made a little easier. Daily tasks such as handling money without relying on someone can be troublesome for them. For that purpose this paper presents a real-time currency detection system designed to assist visually impaired individuals. The proposed model is trained on a dataset containing 30 classes of notes and coins, representing 3 types of currency: US dollar (USD), Euro (EUR), and Bangladeshi taka (BDT). Our approach uses a YOLOv8 nano model with a custom detection head featuring deep convolutional layers and Squeeze-and-Excitation blocks to enhance feature extraction and detection accuracy. Our model has achieved a higher accuracy of 97.73%, recall of 95.23%, f1-score of 95.85% and a mean Average Precision at IoU=0.5 (mAP50(B)) of 97.21\%. Using the voice feedback after the detection would help the visually impaired to identify the currency. This paper aims to create a practical and efficient currency detection system to empower visually impaired individuals independent in handling money.