YOLOv11通过新模块提升检测精度与效率，适合实时视觉应用。
YOLOv11: An Overview of the Key Architectural Enhancements
- 引入C3k2、SPPF和C2PSA等新模块增强特征提取能力
- 在mAP和计算效率上优于前代模型，兼顾精度与轻量化
- 支持从纳米到超大模型，适配边缘设备到高性能场景
本文对YOLOv11——YOLO系列最新迭代的架构进行分析。研究重点包括C3k2（跨阶段部分卷积，核大小为2）、SPPF（快速空间金字塔池化）和C2PSA（并行空间注意力卷积块）等创新组件，这些设计显著提升了模型在目标检测、实例分割、姿态估计及有向目标检测（OBB）等任务中的表现。相比前代模型，YOLOv11在保持较低参数量的同时，实现了更高的平均精度（mAP）与更优的计算效率，展现了良好的性能-复杂度平衡。该模型覆盖从nano到extra-large的多种规模，可灵活部署于边缘设备与高性能计算环境。研究揭示了其在实时计算机视觉领域的潜力与应用前景。
原文摘要 · Abstract (English)
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.
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