Kyeong Won Oh
Papers
2
Total Citations
88
H-Index
2
About
Kyeong Won Oh is a leading researcher in autonomous robotics and computer vision, with a focus on enabling practical, real-world applications. His work bridges the gap between high-performance algorithms and deployable robotic systems, particularly in object detection and maintenance automation. Oh’s seminal 2016 study, "Comparison of faster R-CNN models for object detection" (48 citations), critically evaluated state-of-the-art deep learning models for real-time use in autonomous robots, highlighting the computational bottlenecks—such as the 5fps performance of VGG16 on a K40 GPU—that must be overcome for practical deployment. This work has guided subsequent research in efficient vision systems. In parallel, Oh has made significant contributions to service robotics, exemplified by his 2015 paper on a "Window cleaning system with water circulation for building façade maintenance robot and its efficiency analysis" (40 citations). This study introduced an innovative, water-recycling mechanism that enhances both the sustainability and operational efficiency of building maintenance robots. Through these contributions, Oh has demonstrated a rare ability to advance both the theoretical underpinnings of robotic perception and the engineering of robust, eco-friendly systems, making his work highly influential in the fields of autonomous navigation and field robotics.
Research Focus
Key Achievements
Top Papers
- 1Comparison of faster R-CNN models for object detection48 citations · 2016
- 2