Papers
1
Total Citations
7
H-Index
1
About
Seng Hua Lee is a researcher at the forefront of industrial automation and intelligent manufacturing, with a primary focus on integrating deep learning and computer vision for robotic part handling and geometric analysis. His most-cited work, "Mechanical parts picking through geometric properties determination using deep learning" (2022, 7 citations), introduces a novel system that leverages the YOLOv3 object detection framework to automatically recognize and extract the geometric properties of mechanical components such as bolts and nuts. This contribution directly addresses a critical bottleneck in automated assembly lines—enabling robots to reliably pick and orient parts with varying shapes and sizes. By combining high-speed recognition with geometric property determination, Lee’s approach enhances the precision and efficiency of industrial pick-and-place operations, reducing reliance on manual labor. His research bridges the gap between state-of-the-art deep learning models and practical manufacturing needs, offering scalable solutions for smart factories. With a growing citation footprint, Lee’s work is gaining recognition among engineers and researchers seeking to deploy robust, vision-guided robotic systems in real-world production environments.
Research Focus
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Top Papers
- 1