Xuehu Duan
Papers
1
Total Citations
5
H-Index
1
About
Xuehu Duan is a researcher focused on computer vision and intelligent robotics, with a particular emphasis on real-time object detection and public health applications. His most cited work introduces RMPC-YOLOv7, a novel mask detection algorithm deployed on the Nao robot platform, designed to address the urgent need for automated face mask monitoring during the COVID-19 pandemic. By restructuring the maxpool and convolution layers of the YOLOv7 architecture, Duan’s algorithm significantly improves detection accuracy in dynamic, real-world environments. This contribution not only demonstrates the practical integration of deep learning with humanoid robotics but also highlights the societal relevance of AI-driven safety systems. With 5 citations, this paper has drawn attention from researchers working at the intersection of robotics, public health, and edge AI. Duan’s work exemplifies how targeted algorithmic refinements can enhance the reliability of autonomous surveillance systems, offering a scalable solution for pandemic response and beyond. His research continues to inspire innovations in lightweight, high-accuracy detection models suitable for resource-constrained platforms.
Research Focus
Key Achievements
Top Papers
- 1