A Comprehensive Review of YOLO: From YOLOv1 and Beyond

Abstract

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present a comprehensive analysis of YOLO's evolution, examining the innovations and contributions in each iteration from the original YOLO to YOLOv8 and YOLO-NAS. We start by describing the standard metrics and postprocessing; then, we discuss the major changes in network architecture and training tricks for each model. 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.Comment: 31 pages, 15 figures, 4 tables, submitted to ACM Computing Surveys This version includes YOLO-NAS and a more detailed description of YOLOv5 and YOLOv8. It also adds three new diagrams for the architectures of YOLOv5, YOLOv8, and YOLO-NA