YOLOv9-Based YOLO-Enhanced Smart Glasses for Real-Time Recognition of Pakistani Currency: Empowering the Visually Impaired

Abstract

People who are blind or visually impaired may find it difficult to manage daily tasks, particularly when it comes to knowing the different denominations of cash. We provide an innovative approach to tackle this problem, enabling real-time cash detection through the combination of wearable technology and deep learning. In this work, a deep learning model-integrated smart glasses system powered by a Raspberry Pi IoT device is introduced. The cutting-edge YOLOv9 algorithm is used by the system to accurately recognize cash notes. Training the model included using an extensive dataset of 3,611 photos with seven distinct rupees denominations: 10, 20, 50, 100, 500, 1000, and 5000. The smart glasses immediately alert the user of the denomination when they detect a bank note by means of voice feedback. Our technology boosts the transaction experience and gives visually impaired people more independence in their financial operations, all while maintaining an accuracy rate of up to 95%. As a practical and dependable instrument for daily transactions, the system's lightweight and portable design guarantees simplicity of use in a variety of environments. Providing a reliable and effective method for cash identification, this research marks a substantial breakthrough in assistive technology.