用手机实时识别货币并语音播报，帮视障者独立辨识美金、欧元和塔卡。
Real-Time Currency Detection and Voice Feedback for Visually Impaired Individuals
- 基于YOLOv8 nano改进模型，融合深度卷积与注意力机制提升识别精度。
- 在30类币种上达97.73%准确率，mAP50(B)高达97.21%。
- 专为视障者设计，检测后即时语音反馈，实用性强。
智能手机技术已广泛普及，为视障人士提供了便利。通过手机摄像头与机器学习结合，可实现日常任务的辅助。例如，视障者独立处理现金常面临困难。为此，本文提出一种实时货币识别系统，针对美元（USD）、欧元（EUR）和孟加拉塔卡（BDT）共30类纸币与硬币进行训练。采用YOLOv8 nano模型，配备自定义检测头，包含深层卷积层与挤压-激励模块，以增强特征提取能力。实验结果显示，模型达到97.73%准确率、95.23%召回率、95.85% F1分数，以及97.21%的mAP50(B)。检测结果通过语音反馈，帮助视障者即时识别货币面值，旨在提升其独立处理金钱的能力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。