Sze‐Teng Liong
Papers
8
Total Citations
83
H-Index
5
About
Sze-Teng Liong is a researcher at the forefront of applying deep learning and computer vision to solve real-world industrial and manufacturing challenges. Her work spans defect detection, robotic manipulation, and construction automation, with a focus on bridging the gap between simulated models and practical applications. Notably, her most cited paper, "Automatic Defect Segmentation on Leather with Deep Learning" (37 citations), addresses a critical quality-control problem in the leather industry, where surface defects directly impact material value. She has also made significant contributions to crane-load trajectory prediction using LSTM networks (13 citations), demonstrating her expertise in time-series forecasting for safety-critical operations. Her research extends to cost-effective concrete fabrication for irregular architectural structures (12 citations) and vision-based object grasping for robotics (5 citations). Liong’s work is characterized by its practical impact—she develops systems that improve efficiency in manufacturing, agriculture, and construction, often integrating novel sensor technologies like HoloLens for 3D printing on freeform surfaces. With a growing portfolio of applied AI solutions, Liong is establishing herself as a key innovator in industrial automation and intelligent manufacturing.
Research Focus
Key Achievements
Top Papers
- 1Automatic Defect Segmentation on Leather with Deep Learning37 citations · 2019
- 2
- 3
- 4
- 5Find the centroid: A vision‐based approach for optimal object grasping5 citations · 2021
- 6
- 7
- 8