A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS
This is an incremental review paper that synthesizes existing knowledge about YOLO architectures for researchers and practitioners in computer vision.
This paper provides a comprehensive review of YOLO architectures from YOLOv1 to YOLOv8 and YOLO-NAS, analyzing their innovations and contributions to real-time object detection systems. It summarizes key lessons from YOLO's development and offers perspectives on future research directions.
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 up to YOLOv8, YOLO-NAS, and YOLO with Transformers. 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.