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YOLO has become a central real-time object detection system for robotics,driverless cars, and video monitoring applications. We present a comprehensiveanalysis of YOLO's evolution, examining the innovations and contributions ineach iteration from the original YOLO up to YOLOv8, YOLO-NAS, and YOLO withTransformers. We start by describing the standard metrics and postprocessing;then, we discuss the major changes in network architecture and training tricksfor each model. Finally, we summarize the essential lessons from YOLO'sdevelopment and provide a perspective on its future, highlighting potentialresearch directions to enhance real-time object detection systems.

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