Pantea Keikhosrokiani
Papers
1
Total Citations
2
H-Index
1
About
Pantea Keikhosrokiani is a researcher whose work bridges artificial intelligence and industrial automation, with a particular focus on computer vision and defect detection. Her most-cited paper, "PCB Defect Detection Method Based on Improved RetinaNet" (2023), demonstrates her expertise in applying deep learning to quality control in electronics manufacturing. This work, which has garnered early citations, proposes enhancements to the RetinaNet architecture for identifying defects in printed circuit boards—a critical task for ensuring reliability in modern electronics. While her citation count is still growing, her research addresses a pressing industry need: automating inspection processes to reduce human error and increase efficiency. Keikhosrokiani's contributions lie in optimizing neural network models for real-world manufacturing challenges, making her work relevant to both academic researchers in computer vision and engineers seeking practical solutions. Her focus on improving detection accuracy and speed positions her as an emerging voice in the intersection of AI and industrial quality assurance.
Research Focus
Key Achievements
Top Papers
- 1PCB Defect Detection Method Based on Improved RetinaNet2 citations · 2023