Computer Vision Projects
Welcome to a collection of hands-on computer vision projects that demonstrate how AI can see and understand the world around us. From object detection and tracking to image segmentation, pose estimation, and automated annotation, these projects cover the core techniques powering modern vision systems.
Whether you’re working on retail automation, traffic monitoring, agriculture analytics, manufacturing quality control, or wildlife research, you’ll find practical examples here built with Ultralytics YOLO models i.e. Ultralytics YOLOv8 and YOLO11 and advanced tools like Segment Anything Model 2 (SAM 2). Each project includes code and documentation so you can learn, adapt, and apply these methods to your own real-world applications.
| Project name | Code | Docs | Description |
|---|---|---|---|
| Apple Counting on Conveyor Belt | Code | ✅ | Perform real-time apple counting on conveyor belts using Ultralytics YOLO11. Ideal for automated quality control and inventory management in food processing and packaging industries. |
| Birds Tracking in Air | Code | ✅ | Track multiple birds in flight using advanced YOLO object tracking models to study migration patterns, monitor wildlife behavior, or enhance aviation safety. |
| Bread Counting in Baking Area on Conveyor Belt | Code | ✅ | Implement real-time bread counting during baking and packaging with YOLO-based systems, optimizing production lines and reducing manual labor. |
| Crowd Density Estimation | Code | ✅ | Estimate crowd density in public areas using computer vision and YOLO detection to improve event management, security, and space utilization. |
| Items Counting in Shopping Trolley | Code | ✅ | Automatically count retail items in shopping trolleys for self-checkout automation, retail analytics, and loss prevention using YOLO models. |
| Items Segmentation in Supermarket | Code | ✅ | Perform item segmentation in supermarkets using YOLO’s instance segmentation for real-time shelf monitoring, inventory tracking, and product placement insights. |
| Auto Annotation using SAM 2 | Code | ✅ | Speed up dataset preparation by leveraging Segment Anything Model 2 (SAM2) for automated annotation in retail, manufacturing, and research computer vision workflows. |
FAQ
What makes Ultralytics YOLO models unique?
Ultralytics YOLO models excel in real-time performance, high accuracy, and versatility across tasks like detection, tracking, segmentation, and counting. They are optimized for edge devices and seamlessly integrate into diverse workflows.
How does the tracking module enhance object detection?
The tracking module goes beyond detection by monitoring objects across video frames, providing trajectories and interactions. It's ideal for real-time applications like traffic monitoring, surveillance, and sports analysis.
Can the Object Counting implementation handle dynamic environments?
Yes, the Object Counting implementation is designed for dynamic settings, like conveyor belts or crowded scenes, by accurately detecting and counting objects in real-time, ensuring operational efficiency.