系统梳理从YOLOv1到YOLOv11的实时检测演进，解析其性能与应用突破。
YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges
- 按版本梳理架构创新，揭示速度、精度与部署效率的平衡演进
- 覆盖目标检测、分割、追踪等多任务扩展，支持医疗与工业场景
- 适合关注实时视觉系统研发与应用落地的研究者与工程师
过去十年中，目标检测技术取得了显著进展，以YOLO（You Only Look Once）系列模型为代表，通过统一的端到端检测框架重塑了实时视觉应用格局。从YOLOv1开创性地采用回归方式检测，到最新YOLOv9，各版本持续通过架构与算法优化，在速度、准确率和部署效率之间实现系统性提升。除核心目标检测外，现代YOLO架构已拓展至实例分割、姿态估计、目标跟踪及医学影像、工业自动化等专用领域。本文全面回顾了YOLO家族的发展历程，重点分析其架构革新、性能基准、扩展能力与实际应用案例，批判性评估模型演进路径，并探讨推动其在多元计算机视觉领域持续影响的新兴研究方向。
原文摘要 · Abstract (English)
Over the past decade, object detection has advanced significantly, with the YOLO (You Only Look Once) family of models transforming the landscape of real-time vision applications through unified, end-to-end detection frameworks. From YOLOv1's pioneering regression-based detection to the latest YOLOv9, each version has systematically enhanced the balance between speed, accuracy, and deployment efficiency through continuous architectural and algorithmic advancements.. Beyond core object detection, modern YOLO architectures have expanded to support tasks such as instance segmentation, pose estimation, object tracking, and domain-specific applications including medical imaging and industrial automation. This paper offers a comprehensive review of the YOLO family, highlighting architectural innovations, performance benchmarks, extended capabilities, and real-world use cases. We critically analyze the evolution of YOLO models and discuss emerging research directions that extend their impact across diverse computer vision domains.
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