IntroductionService robot technology is increasingly gaining prominence in the field of artificial intelligence. However, persistent limitations continue to impede its widespread implementation. In this regard, human motion pose estimation emerges as a crucial challenge necessary for enhancing the perceptual and decision-making capacities of service robots.MethodThis paper introduces a groundbreaking model, YOLOv8-ApexNet, which integrates advanced technologies, including Bidirectional Routing Attention (BRA) and Generalized Feature Pyramid Network (GFPN). BRA facilitates the capture of inter-keypoint correlations within dynamic environments by introducing a bidirectional information propagation mechanism. Furthermore, GFPN adeptly extracts and integrates feature information across different scales, enabling the model to make more precise predictions for targets of various sizes and shapes.ResultsEmpirical research findings reveal significant performance enhancements of the YOLOv8-ApexNet model across the COCO and MPII datasets. Compared to existing methodologies, the model demonstrates pronounced advantages in keypoint localization accuracy and robustness.DiscussionThe significance of this research lies in providing an efficient and accurate solution tailored for the realm of service robotics, effectively mitigating the deficiencies inherent in current approaches. By bolstering the accuracy of perception and decision-making, our endeavors unequivocally endorse the widespread integration of service robots within practical applications.

中文翻译：

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机器人位姿估计技术的应用前景：基于YOLOv8-ApexNet探索新方向

简介服务机器人技术在人工智能领域日益受到重视。然而，持续存在的限制继续阻碍其广泛实施。在这方面，人体运动姿态估计成为增强服务机器人感知和决策能力所必需的关键挑战。方法本文介绍了一种突破性的模型YOLOv8-ApexNet，该模型集成了包括双向路由注意（BRA）在内的先进技术和广义特征金字塔网络（GFPN）。 BRA 通过引入双向信息传播机制，有助于捕获动态环境中的关键点间相关性。此外，GFPN 熟练地提取和整合不同尺度的特征信息，使模型能够对各种尺寸和形状的目标做出更精确的预测。结果实证研究结果表明，YOLOv8-ApexNet 模型在 COCO 和 MPII 数据集上的性能显着增强。与现有方法相比，该模型在关键点定位精度和鲁棒性方面表现出明显的优势。讨论本研究的意义在于为服务机器人领域提供高效、准确的解决方案，有效缓解当前方法固有的缺陷。通过提高感知和决策的准确性，我们的努力明确支持服务机器人在实际应用中的广泛集成。