Ultralytics YOLO11 | Training, Inference, Benchmarking, and Deployment Explained! 🚀 - How to Estimate Distance Between Detected Objects with Ultralytics YOLO in Pixels 🚀🤯

Discover how to calculate distances between detected objects in pixels using Ultralytics YOLO. This tutorial guides you through the concept of distance measurement in pixel space, explaining how bounding boxes can be used to estimate object separation in real-time. Since this approach is based on 2D image data, the calculated distances are approximate rather than physically accurate; however, they are still valuable for many computer vision applications. We’ll begin by reviewing the distance calculation documentation, exploring the core logic in Python, and demonstrating how to measure the distance between detected objects using a trained detection model. The video also provides practical examples and discusses how these techniques can be applied to traffic analysis, safety systems, and various other tasks. Chapters: 00:00 - Introduction to distance calculation 00:48 - Overview of distance calculation documentation 01:49 - Understanding pixel space and coordinate mapping 03:30 - Python code for distance calculation 05:29 - Calculating distance between boxes with YOLO detection model 07:22 - Conclusion and key takeaways 🔗 Documentation and code ➡️ https://docs.ultralytics.com/guides/distance-calculation/ Ultralytics YOLO Resources: 💻 GitHub Repository: https://github.com/ultralytics/ 📚 Documentation: https://docs.ultralytics.com/ #distancecalculation #yolo11 #ultralytics #computervision #objectdetection #python