Dive into a comprehensive tutorial series on YOLOv4 object detection using OpenCV and Python for computer vision applications. Learn about the latest advancements in YOLO technology, including improved speed and performance compared to previous versions. Explore the paper "YOLOv4: Optimal Speed and Accuracy of Object Detection" and understand its key concepts without excessive technical jargon. Discover how YOLOv4 works, its development process, approaches used, and performance comparisons with other object detection models. Follow step-by-step tutorials covering installation, running YOLOv4 on images and videos, and implementing it for webcam applications. Gain practical experience with real-world projects such as social distancing monitoring, object tracking with DeepSORT, face recognition attendance systems, and car counting applications. Investigate controversies surrounding YOLOv5 and explore specialized object detection tasks like face mask detection and chess piece recognition. Benefit from insights shared in an interview with OpenCV CEO Satya Mallick and test your skills with unique applications like a Bernie Sanders detector and Cyberpunk 2077 glitch detection. Read more