生物医学工程研究2026，Vol.45Issue(4)：304-310,7.DOI:10.19529/j.cnki.1672-6278.2026.04.06
基于改进YOLOv8n-Pose的人体关键点检测模型
Human keypoint detection model based on improved YOLOv8n-Pose
李宇皓 1仇大伟 1郭振1
作者信息
- 1. 山东中医药大学 医学信息工程学院,济南 250014
- 折叠
摘要
Abstract
To address the problem of low accuracy in human keypoint recognition under complex scenarios such as multiple people and occlusion,we proposed an improved model based on YOLOv8n-Pose.Firstly,the DCNv3 module was introduced to enhance the accuracy of the network while reducing its computational complexity.Then,the spatial and channel synergistic attention(SCSA)mech-anism was introduced to enhance the network's feature extraction capability.Finally,the EIoU loss function was used to accelerate the convergence speed of bounding box detection.The test results of the improved model on the OC_Human dataset showed that,compared with the original model,the accuracy was increased by 2.1％,mAP50 was improved by 3.0％,and floating point operations was re-duced by 0.7％.The proposed model can accurately and effectively identify human keypoint detection in complex scenarios,and meet the requirements for human keypoint detection tasks in multi-person scenarios.关键词
姿态估计/人体关键点/图像处理/检测/注意力机制Key words
Pose estimation/Human keypoint/Image processing/Detection/Attention mechanism分类
医药卫生引用本文复制引用
李宇皓,仇大伟,郭振..基于改进YOLOv8n-Pose的人体关键点检测模型[J].生物医学工程研究,2026,45(4):304-310,7.
