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.

翻译：本综述系统梳理了从YOLOv1到最新发布的YOLOv10这一系列You Only Look Once（YOLO）目标检测算法的发展历程。研究采用逆时序分析方法，从YOLOv10开始，依次回溯YOLOv9、YOLOv8及更早版本，审视各版本在提升实时目标检测速度、精度和计算效率方面的贡献。研究重点阐述了YOLO在五大关键应用领域——汽车安全、医疗健康、工业制造、安防监控和农业——所产生的变革性影响。通过详述后续YOLO版本中的渐进式技术进步，本综述记录了YOLO的演进轨迹，并探讨了各早期版本面临的挑战与局限。这一演进历程指明了YOLO与多模态、情境感知及通用人工智能（AGI）系统相融合的发展方向，预示着未来十年YOLO在人工智能驱动应用领域将产生深远影响。