Jooyeop Han
Papers
2
Total Citations
29
H-Index
2
About
Jooyeop Han is a leading researcher in industrial robotics and automation, specializing in vision-based systems for robotic de-palletizing. His work focuses on overcoming critical challenges in automated logistics, particularly the accurate recognition and handling of boxes in complex, noisy environments. Han’s major contributions include the development of a systematic RGB-D image processing framework that integrates multiple deep learning methods, achieving robust box detection in real-world industrial settings. His 2023 paper on this topic has garnered 23 citations, reflecting its practical significance. He also pioneered a novel approach using Cycle-GAN and Mask-RCNN to enhance detection accuracy under the disruptive effects of tags and chaotic pile arrangements, a method that addresses a key bottleneck in automation. This work, published in 2022, has been cited 6 times and is foundational for improving the reliability of robotic picking systems. Han’s research directly impacts the efficiency of logistics and manufacturing, offering scalable solutions for complex, high-variability environments. His achievements are notable for bridging the gap between advanced computer vision and industrial application, making him a key figure in the evolution of smart automation.
Research Focus
Key Achievements
Top Papers
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- 2