This study presents an architectural analysis of YOLOv11, the latest iteration in the YOLO (You Only Look Once) series of object detection models. We examine the models architectural innovations, including the introduction of the C3k2 (Cross Stage Partial with kernel size 2) block, SPPF (Spatial Pyramid Pooling - Fast), and C2PSA (Convolutional block with Parallel Spatial Attention) components, which contribute in improving the models performance in several ways such as enhanced feature extraction. The paper explores YOLOv11's expanded capabilities across various computer vision tasks, including object detection, instance segmentation, pose estimation, and oriented object detection (OBB). We review the model's performance improvements in terms of mean Average Precision (mAP) and computational efficiency compared to its predecessors, with a focus on the trade-off between parameter count and accuracy. Additionally, the study discusses YOLOv11's versatility across different model sizes, from nano to extra-large, catering to diverse application needs from edge devices to high-performance computing environments. Our research provides insights into YOLOv11's position within the broader landscape of object detection and its potential impact on real-time computer vision applications.

翻译：本研究对YOLO（You Only Look Once）系列目标检测模型的最新版本YOLOv11进行了架构分析。我们深入探讨了该模型的架构创新，包括引入C3k2（采用核尺寸2的跨阶段部分连接）模块、SPPF（快速空间金字塔池化）以及C2PSA（并行空间注意力卷积模块）等组件，这些创新通过增强特征提取等多方面提升了模型性能。本文系统阐述了YOLOv11在目标检测、实例分割、姿态估计和定向目标检测（OBB）等多种计算机视觉任务中的扩展能力。通过对比前代模型，我们评估了该模型在平均精度均值（mAP）和计算效率方面的性能提升，重点关注参数数量与精度之间的平衡关系。此外，研究还探讨了YOLOv11从纳米级到超大型不同规模模型的通用性，可满足从边缘设备到高性能计算环境的多样化应用需求。本研究为理解YOLOv11在目标检测领域的发展定位及其对实时计算机视觉应用的潜在影响提供了重要见解。