Jonghun Yoon
Papers
9
Total Citations
128
H-Index
6
About
Jonghun Yoon is a leading researcher at the intersection of machine vision, robotics, and intelligent manufacturing, whose work is transforming how factories automate complex assembly and logistics tasks. His primary contributions lie in developing novel vision-based methods for 3D profile extraction, object detection, and quality control in robotic systems. Yoon’s most impactful work includes a groundbreaking method for 3D wire harness extraction in robotized assembly (29 citations) and a systematic RGB-D image processing approach for logistics box recognition in de-palletising (23 citations). He has also pioneered real-time welding quality prediction using IR thermal imaging and ANN models (23 citations), addressing critical limitations in traditional evaluation methods. His research extends to automated furniture assembly systems, demonstrating practical applications in IKEA chair component picking and sorting. With a cumulative citation count exceeding 120, Yoon’s work is widely recognized for its practical impact on industrial automation. His recent systematic review of machine vision applications in manufacturing (2025) further solidifies his role as a thought leader in the field, offering comprehensive insights into quality control and predictive diagnostics. Yoon’s innovative integration of deep learning, 3D vision, and robotics continues to push the boundaries of smart manufacturing.
Research Focus
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Top Papers
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