Implementation code and computational results.

Pose estimation is a crucial task in the field of human motion analysis, and detecting poses is a topic of significant interest. Traditional detection algorithms are not only time-consuming and labor-intensive but also suffer from deficiencies in accuracy and objectivity. To address these issues, we propose an improved pose estimation algorithm based on the YOLOv8 framework. By incorporating a novel attention mechanism, SimDLKA, into the original YOLOv8 model, we enhance the model’s ability to selectively focus on input data, thereby improving its decoupling and flexibility. In the feature fusion module of YOLOv8, we replace the original Bottleneck module with the SimDLKA module and integrate it with the C2F module to form the C2F-SimDLKA structure, which more effectively fuses global semantics, especially for medium to large targets. Furthermore, we introduce a new loss function, DCIOU, based on the YOLOv8 loss function, to improve the forward propagation of model training. Results indicate that our new loss function has a 3–5 loss value reduction compared to other loss functions. Additionally, we have independently constructed a large-scale pose estimation dataset, HP, employing various data augmentation strategies, and utilized the open-source COCO and MPII datasets for model training. Experimental results demonstrate that, compared to the traditional YOLOv8, our improved YOLOv8 algorithm increases the mAP value on the pose estimation dataset by 2.7% and the average frame rate by approximately 3 frames. This method provides a valuable reference for pose detection in pose estimation.

姿态估计是人体运动分析领域的核心任务，姿态检测亦是当前广受关注的研究热点。传统检测算法不仅耗时耗力，还在精度与客观性上存在明显不足。为解决上述问题，本文提出一种基于YOLOv8框架的改进型姿态估计算法：通过在原生YOLOv8模型中嵌入新型注意力机制SimDLKA，提升了模型对输入数据的选择性聚焦能力，进而增强其解耦性与灵活性。在YOLOv8的特征融合模块中，我们将原生瓶颈模块（Bottleneck）替换为SimDLKA模块，并将其与C2F模块相结合，构建出C2F-SimDLKA结构，该结构可更高效地融合全局语义信息，尤其针对中大型目标效果更优。此外，我们基于YOLOv8原损失函数引入新型损失函数DCIOU，以优化模型训练的前向传播过程。实验结果显示，相较于其他损失函数，本文提出的DCIOU损失函数可将损失值降低3~5个单位。同时，我们自主构建了大规模姿态估计数据集HP，并采用多种数据增强策略，同时借助开源的COCO与MPII数据集开展模型训练。实验结果表明，相较于传统YOLOv8，本文改进后的YOLOv8算法在姿态估计数据集上的平均精度均值（mean Average Precision, mAP）提升2.7%，平均帧率提升约3帧。该方法为姿态估计领域的姿态检测任务提供了极具参考价值的技术方案。