  This review systematically examines the progression of the You Only Look Once
(YOLO) object detection algorithms from YOLOv1 to the recently unveiled
YOLOv10. Employing a reverse chronological analysis, this study examines the
advancements introduced by YOLO algorithms, beginning with YOLOv10 and
progressing through YOLOv9, YOLOv8, and subsequent versions to explore each
version's contributions to enhancing speed, accuracy, and computational
efficiency in real-time object detection. The study highlights the
transformative impact of YOLO across five critical application areas:
automotive safety, healthcare, industrial manufacturing, surveillance, and
agriculture. By detailing the incremental technological advancements in
subsequent YOLO versions, this review chronicles the evolution of YOLO, and
discusses the challenges and limitations in each earlier versions. The
evolution signifies a path towards integrating YOLO with multimodal,
context-aware, and General Artificial Intelligence (AGI) systems for the next
YOLO decade, promising significant implications for future developments in
AI-driven applications.
