YOLO-Rlepose: Improved YOLO Based on Swin Transformer and Rle-Oks Loss for Multi-Person Pose Estimation

In recent years, there has been significant progress in human pose estimation, fueled by the widespread adoption of deep convolutional neural networks. However, despite these advancements, multi-person 2D pose estimation still remains highly challenging due to factors such as occlusion, noise, and non-rigid body movements. Currently, most multi-person pose estimation approaches handle joint localization and association separately. This study proposes a direct regression-based method to estimate the 2D human pose from a single image. The authors name this network YOLO-Rlepose. Compared to traditional methods, YOLO-Rlepose leverages Transformer models to better capture global dependencies between image feature blocks and preserves sufficient spatial information for keypoint detection through a multi-head self-attention mechanism. To further improve the accuracy of the YOLO-Rlepose model, this paper proposes the following enhancements. Firstly, this study introduces the C3 Module with Swin Transformer (C3STR). This module builds upon the C3 module in You Only Look Once (YOLO) by incorporating a Swin Transformer branch, enhancing the YOLO-Rlepose model's ability to capture global information and rich contextual information. Next, a novel loss function named Rle-Oks loss is proposed. The loss function facilitates the training process by learning the distributional changes through Residual Log-likelihood Estimation. To assign different weights based on the importance of different keypoints in the human body, this study introduces a weight coefficient into the loss function. The experiments proved the efficiency of the proposed YOLO-Rlepose model. On the COCO dataset, the model outperforms the previous SOTA method by 2.11% in AP.