YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series

Abstract This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv12. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv12 and progressing through YOLO11 (or YOLOv11), YOLOv10, YOLOv9, YOLOv8, and subsequent versions to explore each version’s contributions to enhancing speed, detection accuracy, and computational efficiency in real-time object detection. Additionally, this study reviews the alternative versions derived from YOLO architectural advancements of YOLO-NAS, YOLO-X, YOLO-R, DAMO-YOLO, and Gold-YOLO. Moreover, the study…

• NS
  National Science Foundation
• UD
  U.S. Department of Agriculture
• IA
  Innovation and Technology Commission
• NI
  National Institute of Food and Agriculture

  Awards: AWD003473., 1029004, AWD003473, AWD004595