The application prospects of robot pose estimation technology: exploring new directions based on YOLOv8-ApexNet
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 p...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-04-01
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| Series: | Frontiers in Neurorobotics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2024.1374385/full |
| _version_ | 1797221722238222336 |
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| author | XianFeng Tang Shuwei Zhao |
| author_facet | XianFeng Tang Shuwei Zhao |
| author_sort | XianFeng Tang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T13:09:57Z |
| format | Article |
| id | doaj.art-b709047bafe546a18fbde3e8a0567c89 |
| institution | Directory Open Access Journal |
| issn | 1662-5218 |
| language | English |
| last_indexed | 2024-04-24T13:09:57Z |
| publishDate | 2024-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj.art-b709047bafe546a18fbde3e8a0567c892024-04-05T04:58:00ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182024-04-011810.3389/fnbot.2024.13743851374385The application prospects of robot pose estimation technology: exploring new directions based on YOLOv8-ApexNetXianFeng Tang0Shuwei Zhao1Physical Education Department, Zhejiang Wanli University, Ningbo, ChinaPhysical Education Department, Hebei University of Technology, Tianjin, ChinaIntroductionService 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.https://www.frontiersin.org/articles/10.3389/fnbot.2024.1374385/fullservice robotshuman motion pose estimationYOLOv8-ApexNetbidirectional routing attentiongeneralized feature |
| spellingShingle | XianFeng Tang Shuwei Zhao The application prospects of robot pose estimation technology: exploring new directions based on YOLOv8-ApexNet Frontiers in Neurorobotics service robots human motion pose estimation YOLOv8-ApexNet bidirectional routing attention generalized feature |
| title | The application prospects of robot pose estimation technology: exploring new directions based on YOLOv8-ApexNet |
| title_full | The application prospects of robot pose estimation technology: exploring new directions based on YOLOv8-ApexNet |
| title_fullStr | The application prospects of robot pose estimation technology: exploring new directions based on YOLOv8-ApexNet |
| title_full_unstemmed | The application prospects of robot pose estimation technology: exploring new directions based on YOLOv8-ApexNet |
| title_short | The application prospects of robot pose estimation technology: exploring new directions based on YOLOv8-ApexNet |
| title_sort | application prospects of robot pose estimation technology exploring new directions based on yolov8 apexnet |
| topic | service robots human motion pose estimation YOLOv8-ApexNet bidirectional routing attention generalized feature |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2024.1374385/full |
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