舰船电子工程2026，Vol.46Issue(3)：38-44,7.DOI:10.3969/j.issn.1672-9730.2026.03.009
基于改进YOLOv8的重参数化人体姿态估计算法
Reparameterized Human Pose Estimation Algorithm Based on Improved YOLOv8
摘要
Abstract
In order to cope with the common challenges in human pose estimation such as occlusion,complex background and body type diversity,the YOLOv8-DRS model is proposed based on the YOLOv8 model.Firstly,DBB reparameterization module is introduced into the backbone network to improve C2f,optimize parameter settings,and effectively enhance feature extraction capa-bility without increasing the number of parameters.Secondly,RFAPose is adopted in the detection head to emphasize the impor-tance of different features,so as to improve the accuracy of key point detection under occlusive and complex background conditions.Finally,SIoU loss function is introduced to improve the model convergence ability and maintain high accuracy when dealing with di-verse body shapes and poses.The experimental results on the COCO human pose estimation dataset show that the accuracy P is in-creased by 0.2%,the recall rate R is increased by 1.8%,and the AP50 is increased by 1.5%,which effectively improves the accura-cy of the model.关键词
深度学习/姿态估计/重参数化/YOLOv8Key words
deep learning/pose estimation/reparameterization/YOLOv8分类
信息技术与安全科学引用本文复制引用
白凯博,杨瑞峰,郭晨霞..基于改进YOLOv8的重参数化人体姿态估计算法[J].舰船电子工程,2026,46(3):38-44,7.基金项目
山西省中央引导地方科技发展自由探索类基础研究项目(编号:YDZJSX20231A032)资助. （编号:YDZJSX20231A032）
