首页|Hebei University of Technology Reports Findings in Robotics (The application pro spects of robot pose estimation technology: exploring new directions based on YO LOv8-ApexNet)
Hebei University of Technology Reports Findings in Robotics (The application pro spects of robot pose estimation technology: exploring new directions based on YO LOv8-ApexNet)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Robotics is the subjec t of a report. According to news reportingout of Tianjin, People’s Republic of China, by NewsRx editors, research stated, “Service robot technologyis increasi ngly gaining prominence in the field of artificial intelligence. However, persis tent limitationscontinue to impede its widespread implementation.”Our news journalists obtained a quote from the research from the Hebei Universit y of Technology,“In this regard, human motion pose estimation emerges as a cruc ial challenge necessary for enhancingthe perceptual and decision-making capacit ies of service robots. This paper introduces a groundbreakingmodel, YOLOv8-Apex Net, which integrates advanced technologies, including Bidirectional Routing Attention (BRA) and Generalized Feature Pyramid Network (GFPN). BRA facilitates the capture of interkeypointcorrelations within dynamic environments by introduci ng a bidirectional information propagationmechanism. Furthermore, GFPN adeptly extracts and integrates feature information across different scales,enabling th e model to make more precise predictions for targets of various sizes and shapes . Empiricalresearch findings reveal significant performance enhancements of the YOLOv8-ApexNet model across theCOCO and MPII datasets. Compared to existing me thodologies, the model demonstrates pronouncedadvantages in keypoint localizati on accuracy and robustness. The significance of this research lies inproviding an efficient and accurate solution tailored for the realm of service robotics, e ffectively mitigatingthe deficiencies inherent in current approaches.”
TianjinPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano-robotRobotRoboticsTechnology
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