Research on Lightweight Model of Multi-person Pose Estimation Based on Improved YOLOv8s-Pose

To address the issues of high computational load and slow detection speed in existing human pose estimation models, this paper proposes a lightweight improved algorithm based on the YOLOv8s-Pose model. Firstly, a lightweight module C2f-GhostNetBottleNeckV2 is introduced into the backbone to replace the original C2f, reducing the number of parameters. This paper also introduces the Non_Local attention mechanism to integrate the position information of human key points in the image into the channel dimension, thereby enhancing the efficiency of feature extraction and mitigating the accuracy degradation issues that often occur after model lightweighting. Furthermore, the weighted bidirectional feature pyramid network is incorporated into the neck layer to improve the model’s feature fusion capabilities, ensuring a good balance when processing features of different scales. A small object detection head is then added to the network to reduce the missed detection of small objects. Lastly, the CIOU loss function is replaced with Focal-EIOU to enhance the accuracy of human key point regression. Experimental results show that the improved model reduces the number of parameters by 9.3%, and compared with the original model on the COCO2017 human key points dataset, it achieves an improvement of 0.4 percentage points in [email protected] and an improvement of 0.6 percentage points in [email protected]:0.95. Therefore, the proposed lightweight improvement algorithm not only reduces the number of model parameters but also enhances the accuracy of human pose estimation algorithms, especially for small target detection, which provides an effective means to achieve real-time and accurate pose estimation.