Yujian Wen
Papers
1
Total Citations
2
H-Index
1
About
Yujian Wen’s research focuses on advancing intelligent robotics through computer vision and deep learning, with a particular emphasis on object detection for healthcare applications. His most cited work, “Pyramid Transformer: A Multi-size Object Detection Model with Limited Device Requirements for the Nursing Robot” (2022), addresses a critical challenge in assistive robotics: detecting objects of varying scales in real-world nursing environments while maintaining computational efficiency. The proposed Pyramid Transformer architecture, comprising three integrated modules, enables nursing robots to accurately identify small, medium, and large objects despite hardware constraints—a breakthrough for deploying AI in resource-limited care settings. With 2 citations, this paper has already influenced subsequent work in lightweight transformer models for robotics. Wen’s contributions bridge the gap between high-performance detection algorithms and practical deployment on embedded systems, making autonomous nursing assistance more viable. His work exemplifies how tailored deep learning solutions can enhance human-robot interaction in healthcare, potentially improving patient care and reducing caregiver burden.
Research Focus
Key Achievements
Top Papers
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