现代电子技术2026，Vol.49Issue(19)：1-9,9.DOI:10.16652/j.issn.1004-373X.2026.19.001
基于改进YOLOv11-Pose的实时多人姿态估计算法
Real-time multi-person pose estimation algorithm based on improved YOLOv11-Pose
摘要
Abstract
In view of the insufficient robustness in keypoint localization and low efficiency in feature fusion for 2D human pose estimation in complex scenarios,this paper proposes MCM-YOLO Person,a real-time multi-person pose estimation algorithm based on improved YOLOv11-Pose.A weight-reused multi-scale dilated convolution attention residual(MSDAR)module is designed to effectively extract local features of human keypoints,which significantly improves localization accuracy.An improved cross-context dynamic fusion(CCDF)module is designed to effectively enhance the robustness of keypoint detection in scenarios involving occlusion and complex poses.Additionally,a multi-feature dynamic guided fusion(MFDGF)module is constructed to achieve efficient multi-scale feature fusion,which strengthens the model adaptability to complex human structures and pose variations.Experiments on the MS COCO 2017 keypoint detection dataset demonstrate that the proposed model achieves a 2.1%improvement in average precision(AP)over the baseline YOLOv11-Pose,without compromising inference efficiency.The proposed strategy is also effective on a larger model,attaining an AP of 71.2%and outperforming most mainstream methods.关键词
姿态估计/YOLOv11/注意力机制/特征融合/多尺度特征/关键点检测Key words
pose estimation/YOLOv11/attention mechanism/feature fusion/multi-scale feature/keypoint detection分类
信息技术与安全科学引用本文复制引用
谭光兴,顾庆朔,郭家梁..基于改进YOLOv11-Pose的实时多人姿态估计算法[J].现代电子技术,2026,49(19):1-9,9.基金项目
国家自然科学基金项目(61563005) （61563005）
