The application prospects of robot pose estimation technology: exploring new directions based on YOLOv8-ApexNet - Tang, XianFeng; Zhao, Shuwei (Frontiers in Neurorobotics, 2024)
The service robot technology is gaining prominence in the field of artificial intelligence.Nonetheless, persistent limitations continue to impede its broad implementation. In this regard, human motion pose estimation stands out as a pivotal challenge essential for augmenting the perceptual and decision-making capacities of service robots. This paper introduces a pioneering model, YOLOv8-ApexNet, which integrates cutting-edge technologies including Bidirectional Routing Attention (BRA) and Generalized Feature Pyramid Network (GFPN). BRA, in particular, 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 varying scales, empowering the model to render more precise predictions for targets of diverse sizes and shapes. Empirical findings unveil substantial enhancements in performance across the COCO and MPII datasets, underscoring pronounced advantages in keypoint localization accuracy and robustness compared to extant methodologies.The significance of this research lies in furnishing 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.