Abstract
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 this http URL work formalizes YOLOv11 in a research context, providing a clear reference for future studies.
Abstract (translated)
YOLOv11是You Only Look Once（YOLO）系列实时目标检测器的最新迭代版本，引入了新型架构模块以提升特征提取和小目标检测能力。本文详细分析了YOLOv11，涵盖其主干网络、颈部网络和头部网络组件。模型的关键创新——C3K2模块、空间金字塔池化快速版（SPPF）以及C2PSA（带空间注意力的跨阶段部分）模块——在保持速度的同时增强了空间特征处理能力。我们将YOLOv11在标准基准测试上的性能与先前YOLO版本进行了对比，突出了平均精度均值（mAP）和推理速度的改进。结果表明，YOLOv11在无需牺牲实时性的前提下实现了更优精度，使其非常适合自动驾驶、监控和视频分析等应用。本研究将YOLOv11置于研究语境中，为未来研究提供了清晰参考。
URL
https://arxiv.org/abs/2604.03349
