Abstract: As the first single-stage object detection algorithm in the era of deep learning, YOLO has sparked a wave of enthusiasm in the field of computer vision with its powerful and unique paradigm, and has become a milestone achievement in object detection algorithms. It is still a typical algorithm that achieves the best balance between speed and accuracy, and is widely used in industrial fields such as autonomous driving and intelligent vision systems. In the past eight years, driven by deep learning technology, YOLO methods have developed rapidly and have profound impact on the entire field of object detection. This paper conducts an in-depth investigation of the YOLO method related work from the perspective of technological evolution, comprehensively summarizing the innovation and contributions of each iteration from the initial YOLO v1 to the latest YOLO v9 and YOLO v10. Based on the significant technological improvements at different time points, the YOLO method is divided into four parts: early basic YOLO, standard version YOLO, standard improvement YOLO, and unique improvement YOLO. The unique perspectives of the improvement methods in each period are introduced in detail. In addition, the dataset and indicators for evaluating the YOLO method are summarized, and detailed experimental results of different versions of YOLO and different models of the same version of YOLO are collected. The development and changes of YOLO are summarized from both macro and micro levels. Through analysis, the differences and inherent connections in the development framework, backbone network architecture, and prior box usage among different versions of YOLO are revealed, emphasizing the importance of balancing speed and accuracy in YOLO. Finally, through systematic review, the future development trends of YOLO method is summarized.

Key words: deep learning, computer vision, object detection, YOLO method

摘要： 作为深度学习时代首个单阶段目标检测算法，YOLO以其强大且独特的范式在计算机视觉领域掀起了一股热潮，并成为目标检测算法的里程碑式成果,至今为止仍是在速度与精度之间实现最佳平衡的典型算法，广泛应用于自动驾驶、智能视觉系统等工业领域。过去的八年里，在深度学习技术的驱动下， YOLO方法有了快速发展并对整个目标检测领域产生深远影响。从技术进化角度深入调查YOLO方法相关工作，对最初的YOLO v1到最新的YOLO v9与YOLO v10每一次迭代创新和贡献进行全面总结，根据不同时间节点的和技术的重大改进将YOLO方法分为早期基础YOLO、标准版本YOLO、标准改进YOLO和独特改进YOLO四部分，详细介绍每个时期改进方法的独特视角。此外，总结评估YOLO方法的数据集与指标，收集了不同版本YOLO、同一版本YOLO不同型号的详细实验结果，从宏观层面与微观层面归纳YOLO的发展变化，通过分析揭示各版本YOLO之间的开发框架、骨干网络架构、先验框使用情况等技术的差异和内在联系，强调了YOLO在速度与准确率之间平衡的重要性。最后通过系统的梳理，凝练YOLO方法未来的发展趋势。

关键词: 深度学习, 计算机视觉, 目标检测, YOLO方法