Real-time object detection is a critical skill in modern computer vision, powering applications from autonomous vehicles to security systems, robotics, industrial automation, and AI-powered surveillance. In this comprehensive course, “Real-Time Object Detection with YOLOv11”, you’ll learn how to implement state-of-the-art object detection systems using the latest YOLOv11 architecture and advanced deep learning techniques.

Starting with the fundamentals, you’ll explore computer vision concepts, image processing techniques, and practical applications of YOLO algorithms. You’ll gain hands-on experience with OpenCV, Python, and Roboflow, learning how to preprocess images, annotate datasets, and integrate detection models into real-world applications efficiently.

The course guides you through training YOLOv11 on custom datasets, including feature extraction, dataset preparation, model training, and evaluation. You’ll also master advanced techniques like instance segmentation, object tracking with BotSort and ByteTrack, and deploying models for real-time performance in dynamic environments.

By the end of this course, you’ll be able to build robust, scalable, and efficient real-time object detection systems, integrate them with practical workflows, and leverage deep learning frameworks to solve complex computer vision problems. Whether you’re a developer, data scientist, AI enthusiast, or student, this course equips you with the skills to create cutting-edge AI vision applications, automated systems, and intelligent solutions using YOLOv11.

Who this course is for:

• Developers and programmers interested in building real-time computer vision applications
• Students and researchers in computer vision, robotics, or artificial intelligence looking to enhance their practical skills
• Practitioners wanting to upgrade their skills using the latest YOLO version
• Professionals in AI, robotics, or surveillance technology seeking to implement object detection pipelines in real-world projects