Authors
Shaobin Cai, Han Xu, Wanchen Cai, Yuchang Mo, Liansuo Wei
Published in
Scientific reports. Volume 15. Issue 1. Pages 15284. May 01, 2025. Epub May 01, 2025.
Abstract
Deep neural networks are used to accurately detect, estimate, and predict human body poses in images or videos through deep learning-based human pose estimation. However, traditional multi-person pose estimation methods face challenges due to partial occlusions and overlaps between multiple human bodies and body parts. To address these issues, we propose EE-YOLOv8, a human pose estimation network based on the YOLOv8 framework, which integrates Efficient Multi-scale Receptive Field (EMRF) and Expanded Feature Pyramid Network (EFPN). First, the EMRF module is employed to further enhance the model's feature representation capability. Second, the EFPN optimizes cross-level information exchange and improves multi-scale data integration. Finally, Wise-IoU replaces the traditional Intersection over Union (IoU) to improve detection accuracy through precise overlap measurement between predicted and ground-truth bounding boxes. We evaluate EE-YOLOv8 on the MS COCO 2017 dataset. Compared to YOLOv8-Pose, EE-YOLOv8 achieves an AP of 89.0% at an IoU threshold of 0.5 (an improvement of 3.3%) and an AP of 65.6% over the IoU range of 0.5-0.95 (an improvement of 5.8%). Therefore, EE-YOLOv8 achieves the highest accuracy while maintaining the lowest parameter count and computational complexity among all analyzed algorithms. These results demonstrate that EE-YOLOv8 exhibits superior competitiveness compared to other mainstream methods.
PMID:
40312474
Bibliographic data and abstract were imported from PubMed on 02 May 2025.
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