About This Architecture

AI-powered smart currency detection system for visually impaired users combines Raspberry Pi 5 edge computing with YOLOv8 deep learning to identify Indian currency denominations in real time. Image acquisition flows through preprocessing (resize, noise removal, contrast enhancement) into YOLOv8 feature extraction and object detection, classifying notes from 10 to 500 rupees with confidence scoring. pyttsx3 offline text-to-speech converts recognition results to voice feedback through speakers, enabling hands-free operation via GPIO push button. This architecture demonstrates how edge AI eliminates cloud latency and privacy concerns for accessibility applications. Fork and customize this diagram on Diagrams.so to adapt the pipeline for other currency systems or assistive use cases.

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How do you build a real-time currency detection system for visually impaired users using YOLOv8 and Raspberry Pi?

This diagram shows a complete edge AI pipeline where a Raspberry Pi 5 runs YOLOv8 object detection on camera images to identify Indian currency denominations (10–500 rupees), then converts results to voice feedback via pyttsx3 offline text-to-speech. Image preprocessing (resize, noise removal, contrast enhancement) optimizes detection accuracy while eliminating cloud latency.

AI Smart Currency Detection Hardware System

OCIadvancedYOLOv8Raspberry Piedge AIassistive technologyobject detection
Domain: Ml PipelineAudience: Edge AI engineers building assistive technology solutions with YOLOv8 and Raspberry Pi
1 views0 favoritesPublic

Created by

August 3, 2026

Updated

August 15, 2026 at 5:32 PM

Type

data pipeline

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