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.

翻译：YOLO已成为机器人、无人驾驶汽车及视频监控应用中核心的实时目标检测系统。本文对YOLO的演进历程进行了全面分析，系统考察了从原始YOLO到YOLOv8及YOLO-NAS各版本中的创新点与技术贡献。我们首先阐述了标准评估指标与后处理技术；继而逐一讨论各模型在网络架构与训练技巧层面的重大变革。最后，我们总结了YOLO发展历程中的关键经验教训，并对其未来发展方向进行了展望，指出了增强实时目标检测系统的潜在研究路径。