A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We start by describing the standard metrics and postprocessing; then, we discuss the major changes in network architecture and training tricks for each model.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 We present a comprehensive analysis of YOLO’s evolution, examining the innovations and contributions in each iteration from the original YOLO up to YOLOv8, YOLO-NAS, and YOLO with transformers.
• 2 We start by describing the standard metrics and postprocessing; then, we discuss the major changes in network architecture and training tricks for each model.
• 3 Finally, we summarize the essential lessons from YOLO’s development and provide a perspective on its future, highlighting potential research directions to enhance real-time object detection systems.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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