Lina Xing
Papers
1
Total Citations
5
H-Index
1
About
Lina Xing is a leading researcher in the field of intelligent manufacturing and industrial quality control, with a primary focus on automated defect detection for printed circuit boards (PCBs) using advanced deep learning and computer vision techniques. Her most cited work, "New PCB Defect Identification and Classification Method Combining MobileNet Algorithm and Improved YOLOv4 Model" (2022, 5 citations), introduces a novel hybrid approach that integrates lightweight MobileNet architectures with an enhanced YOLOv4 detection framework. This method significantly improves the speed and accuracy of identifying and classifying common PCB defects—such as soldering faults, scratches, and misalignments—critical for the quality assurance of emerging 3C products like smartwatches and wearable devices. Xing’s contributions are particularly impactful in enabling real-time, edge-deployable inspection systems that reduce reliance on manual inspection, thereby lowering costs and increasing production efficiency. Her work has garnered attention from both academia and industry, with citations reflecting its practical relevance in smart manufacturing. Xing continues to advance the intersection of AI and industrial automation, positioning her as a key innovator in next-generation quality control technologies.
Research Focus
Key Achievements
Top Papers
- 1