Haonan Chen
Papers
1
Total Citations
5
H-Index
1
About
Haonan Chen is a researcher whose work sits at the intersection of computer vision, robotics, and public health technology. Chen’s most notable contribution is the development of RMPC-YOLOv7, a novel mask detection algorithm designed for deployment on Nao robotic platforms. This work, published in 2023, directly addressed the urgent need for automated public health compliance monitoring during the COVID-19 pandemic. By restructuring the Maxpool and Convolution layers of the YOLOv7 architecture, Chen’s algorithm achieves more accurate detection of face masks in real-world environments, demonstrating how deep learning can be adapted for resource-constrained robotic systems. With 5 citations to date, this paper has already begun influencing the intersection of social robotics and epidemiological safety measures. Chen’s research exemplifies the practical application of advanced object detection models, bridging the gap between theoretical computer vision and deployable, socially beneficial technology. This work positions Chen as a promising voice in the growing field of AI-driven public health robotics.
Research Focus
Key Achievements
Top Papers
- 1