Wenting Wei
Papers
1
Total Citations
22
H-Index
1
About
Wenting Wei is a researcher focused on advancing computer vision and automated quality inspection in industrial manufacturing. Her most cited work, "An improved SegNet network model for accurate detection and segmentation of car body welding slags" (2022), has garnered 22 citations, reflecting its practical significance. In this study, she enhanced the SegNet deep learning architecture to precisely identify and segment welding slag on car bodies—a critical task for ensuring structural integrity and surface quality in automotive production. By optimizing the network for real-world industrial conditions, Wei's contribution directly addresses challenges in automated defect detection, reducing reliance on manual inspection and improving efficiency. Her work bridges the gap between cutting-edge semantic segmentation techniques and applied manufacturing needs, demonstrating how tailored neural network improvements can yield robust, deployable solutions. This research not only advances the field of industrial computer vision but also provides a valuable benchmark for similar quality control tasks in other sectors. Wei's focus on practical, high-impact applications marks her as a promising contributor to intelligent manufacturing and automated inspection systems.
Research Focus
Key Achievements
Top Papers
- 1