Papers
1
Total Citations
11
H-Index
1
About
Zeju Wu is a researcher specializing in computer vision and industrial automation, with a particular focus on deep learning-based defect detection systems. Their most cited work, "Tire Defect Detection Based on Faster R-CNN" (2020), has garnered 11 citations and represents a significant contribution to quality control in manufacturing. By adapting the Faster R-CNN architecture for tire inspection, Wu demonstrated how convolutional neural networks can effectively identify subtle surface and structural defects in real-time production environments, addressing a critical need for non-destructive testing in the automotive industry. This research bridges the gap between advanced computer vision techniques and practical industrial applications, offering a scalable solution that reduces human error and inspection costs. Wu's work has implications beyond tire manufacturing, providing a methodological framework that can be extended to defect detection in other materials and products. Their research continues to influence the development of automated visual inspection systems, making manufacturing processes more reliable and efficient.
Research Focus
Key Achievements
Top Papers
- 1Tire Defect Detection Based on Faster R-CNN11 citations · 2020