This paper presents an architectural analysis of YOLOv12, a significant advancement in single-stage, real-time object detection building upon the strengths of its predecessors while introducing key improvements. The model incorporates an optimised backbone (R-ELAN), 7x7 separable convolutions, and FlashAttention-driven area-based attention, improving feature extraction, enhanced efficiency, and robust detections. With multiple model variants, similar to its predecessors, YOLOv12 offers scalable solutions for both latency-sensitive and high-accuracy applications. Experimental results manifest consistent gains in mean average precision (mAP) and inference speed, making YOLOv12 a compelling choice for applications in autonomous systems, security, and real-time analytics. By achieving an optimal balance between computational efficiency and performance, YOLOv12 sets a new benchmark for real-time computer vision, facilitating deployment across diverse hardware platforms, from edge devices to high-performance clusters.

本研究针对YOLOv12进行了架构分析，填补了单阶段实时物体检测领域的重要空白。论文提出了一种优化的骨干网络和引入了7x7可分离卷积及基于FlashAttention的区域注意力，显著提升了特征提取效率和检测的鲁棒性。实验结果显示YOLOv12在均值平均精度和推理速度上均有显著提高，为自动化系统、安全监控和实时分析等应用提供了高效的解决方案。

YOLOv12：关键架构特征的分析