Empty Shelf Detection using YOLO & Roboflow
Out-of-stock shelves cost the retail industry billions every year. Manual shelf auditing is slow, inefficient, and unreliable — so we built an AI-powered solution to solve this problem.
What We Built
A real-time Empty Shelf Detection system that uses a custom-trained YOLO object detection model to automatically identify empty or out-of-stock spaces on retail shelves through a live mobile camera feed.
Tech Stack
- YOLO — Real-time object detection
- Roboflow — Dataset annotation, augmentation, and training
- YOLOvX App — Live mobile deployment
- Custom Dataset — Real retail shelf images manually annotated
How It Works
- Point the camera at a retail shelf
- The YOLO model processes the live feed
- Bounding boxes appear on empty shelf spaces
- Total empty space count is displayed instantly
Real-World Impact
- Faster restocking decisions for store staff
- Reduces revenue loss caused by out-of-stock situations
- Eliminates the need for manual shelf auditing
- Scalable for CCTV-based fully automated monitoring systems
What We Learned
This project gave us hands-on experience in:
- Computer Vision
- Dataset Preparation
- Data Annotation
- Model Training
- Real-Time AI Deployment
- Mobile-Based Detection Systems
- This video showcases a YOLOvX-based smart retail shelf monitoring system designed to detect and identify empty spaces on supermarket shelves in real time. Using advanced object detection and computer… | utsav patil
We are proud of what we built and excited to continue exploring AI-powered retail automation solutions.