基于YOLOv8框架的人类姿势估计网络,具有高效的多尺度受体场和扩展功能金字塔网络.
Shaobin Cai1,2, Han Xu3, Wanchen Cai4
1College of Information Engineering, Huzhou University, Huzhou, China. caishaobin@zjhu.edu.cn.
Scientific reports
|May 1, 2025
概括
EE-YOLOv8通过集成高效多尺度感应场 (EMRF) 和扩展特征金字塔网络 (EFPN) 模块来增强人类姿势估计. 这种深度学习方法提高了检测人体姿势的准确性和效率,即使有遮.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 传统的多人姿势估计与遮和身体部分重叠作斗争.
- 基于深度学习的方法为图像和视频中的人类姿势估计提供了先进的解决方案.
研究的目的:
- 推出EE-YOLOv8,这是一款旨在克服现有方法局限性的新型人体姿势估计网络.
- 为了增强特征表示,优化多尺度数据集成,提高检测准确性.
主要方法:
- 开发了EE-YOLOv8,将高效多尺度受体场 (EMRF) 和扩展特征金字塔网络 (EFPN) 集成到YOLOv8框架中.
- 采用智能IoU来取代传统的交叉路口对联盟 (IoU) 进行更精确的界限框重叠测量.
- 在MS COCO 2017数据集上评估的性能.
主要成果:
- 在0.5的IOU门下,EE-YOLOv8实现了89.0%的AP,比YOLOv8-Pose有3.3%的改善.
- 在0.5-0.95的IOU范围内实现了65.6%的AP,改善了5.8%.
- 以最低的参数数量和计算复杂度证明了卓越的准确性.
结论:
- EE-YOLOv8显著优于现有的主流人类姿势估计方法.
- 拟议的网络为复杂的姿势估计任务提供了高精度和计算效率的解决方案.
- 在基于深度学习的人类姿势估计领域,EE-YOLOv8表现出强大的竞争力.
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