YOLOv11 解密：高性能目标检测的实用指南
YOLOv11 Demystified: A Practical Guide to High-Performance Object Detection
AI总结：
本文解析YOLOv11的架构，介绍其创新模块提升特征提取和小目标检测能力，并通过实验展示其在精度和推理速度上的优势。
摘要：
YOLOv11是实时目标检测YOLO系列的最新版本，引入了新的架构模块以改进特征提取和小目标检测。本文详细分析了YOLOv11的骨干网络、颈部和头部组件。关键创新包括C3K2块、空间金字塔池化-快速（SPPF）和C2PSA（跨阶段部分与空间注意力）模块，这些模块在保持速度的同时增强了空间特征处理能力。我们比较了YOLOv11在标准基准上的性能，突显了mAP和推理速度的改进。我们的结果表明，YOLOv11在不牺牲实时能力的情况下实现了更高的准确性，使其适用于自动驾驶、监控和视频分析等应用。本文将YOLOv11正式纳入研究领域，为未来研究提供了清晰的参考。
英文摘要：
YOLOv11 is the latest iteration in the You Only Look Once (YOLO) series of real-time object detectors, introducing novel architectural modules to improve feature extraction and small-object detection. In this paper, we present a detailed analysis of YOLOv11, including its backbone, neck, and head components. The model key innovations, the C3K2 blocks, Spatial Pyramid Pooling - Fast (SPPF), and C2PSA (Cross Stage Partial with Spatial Attention) modules enhance spatial feature processing while preserving speed. We compare YOLOv11 performance to prior YOLO versions on standard benchmarks, highlighting improvements in mean Average Precision (mAP) and inference speed. Our results demonstrate that YOLOv11 achieves superior accuracy without sacrificing real-time capabilities, making it well-suited for applications in autonomous driving, surveillance, and video analytics.This work formalizes YOLOv11 in a research context, providing a clear reference for future studies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
