Kwang Moo Yi

University of British Columbia, Seoul National University

Papers

Total Citations

H-Index

About

Kwang Moo Yi is a leading researcher in computer vision and deep learning, with a focus on applying these technologies to industrial automation and robotics. His work bridges the gap between theoretical machine learning and practical engineering solutions, particularly in the domain of welding automation. Yi's most cited paper, "Vision-based seam tracking for GMAW fillet welding based on keypoint detection deep learning model" (2024, 17 citations), introduces a novel deep learning approach that enables pre-programmed welding robots to adapt to noisy, real-world environments. By using keypoint detection for seam tracking, this work significantly improves weld quality and efficiency in small and medium batch production—a long-standing challenge in manufacturing. Earlier in his career, Yi explored human-robot interaction with "Implementation of home automation system using a PDA based mobile robot" (2009, 4 citations), proposing an architecture for mobile robots to manage home appliances autonomously. His research demonstrates a consistent commitment to making robots more adaptive and intelligent, from factory floors to smart homes. Yi's contributions are particularly valuable for students and researchers interested in the intersection of deep learning, robotics, and industrial automation.