Shelf Scanning Application
Project Overview
The Shelf Scanning Application is an AI-powered mobile solution designed to transform how retail stores conduct shelf audits and compliance checks. By leveraging computer vision technology, the app automates the traditionally manual and time-consuming process of verifying shelf conditions, product placement, and pricing accuracy.
The Challenge
Retail shelf compliance is a critical but labor-intensive process. Store teams spend countless hours manually checking shelves for out-of-stock items, misplaced products, and pricing errors. This manual approach is not only time-consuming but also prone to human error and inconsistency.
The challenge was to create a solution that could accurately analyze shelf conditions in real-time, provide actionable insights, and integrate seamlessly with existing store workflows.
Key Features
Computer Vision Integration
At the heart of the application is a sophisticated computer vision system powered by Azure Cognitive Services. The system can identify products, detect gaps on shelves, verify price tags, and flag compliance issues—all from a simple photo capture.
Automated Reporting
The application automatically generates detailed compliance reports, highlighting issues that need attention. Reports include visual annotations, severity rankings, and suggested actions, making it easy for store teams to prioritize their work.
Real-Time Analytics
A comprehensive analytics dashboard provides store managers with real-time visibility into shelf compliance across all aisles and departments. Historical trends help identify recurring issues and measure improvement over time.
Intuitive Mobile Interface
The mobile app was designed with store associates in mind. A simple, intuitive interface allows users to quickly scan shelves, review results, and take action—all without extensive training.
Technical Implementation
The application was built using React Native for the mobile frontend, with Azure Cognitive Services providing the computer vision capabilities. The backend, built with Node.js, handles image processing, data storage, and analytics computations.
Special attention was paid to optimizing image capture and processing for mobile devices. The app guides users to capture optimal images and provides immediate feedback on image quality before processing.
AI & Machine Learning
The computer vision models were trained to recognize thousands of products specific to grocery retail. The system continuously improves through feedback loops, learning from corrections made by store teams to increase accuracy over time.
Results & Impact
The Shelf Scanning Application has dramatically reduced the time required for shelf audits while improving accuracy. Store teams can now complete comprehensive shelf checks in a fraction of the time, allowing them to focus on customer service and other high-value activities.