Retail Shelf Analytics System

Project Overview

An AI-powered system designed for automated product detection and counting on retail shelves. Using YOLOv8, the system enhances inventory management by providing real-time insights into stock levels, shelf arrangement, and out-of-stock alerts

Technologies Used

YOLOv8 – Detects and classifies products on store shelves

OpenCV – Enhances image processing and object tracking

Python – Used for model development, data processing, and deployment

Edge Computing or Cloud Integration – Supports real-time data processing and analytics

How It Works

Image Capture: The system processes images from cameras installed on retail shelves

Product Detection: YOLOv8 identifies and locates products within the image

Counting & Stock Analysis: The system counts the number of items per category and detects empty spaces

Data Reporting: The extracted data is visualized in dashboards or integrated with inventory management systems

Alerts & Automation: The system can send notifications when stock levels are low or when products are misplaced

This solution helps retailers reduce manual inventory checks, optimize shelf organization, and improve stock availability, leading to increased sales and operational efficiency