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、自动驾驶汽车和视频监控应用中主流的实时目标检测系统。本文对YOLO的演进过程进行了全面分析，探讨了从原始YOLO到YOLOv8及YOLO-NAS各版本中的创新与贡献。我们首先描述了标准评估指标与后处理方法；随后讨论了每个模型在网络架构和训练技巧方面的主要变革。最后，我们总结了YOLO发展中的关键经验，并对其未来前景进行了展望，指出了提升实时目标检测系统的潜在研究方向。