Anastasia Lampoglou
Papers
1
Total Citations
20
H-Index
1
About
Anastasia Lampoglou is a researcher at the intersection of robotics, computer vision, and materials science, with a primary focus on automated defect detection and intelligent manufacturing. Her most cited work, "Towards Robotic Marble Resin Application: Crack Detection on Marble Using Deep Learning" (2022, 20 citations), addresses a critical industrial challenge: the manual inspection of cracks on marble surfaces, which is both time-consuming and error-prone. By leveraging deep learning for crack segmentation, Lampoglou proposes a machine vision system capable of detecting defects with high accuracy, paving the way for robotic resin application and automated quality control in stone processing. This contribution is part of a broader effort to integrate AI-driven visual inspection into production lines, reducing human error and increasing efficiency. Her research has implications beyond marble, extending to crack detection on buildings, roads, and aircraft surfaces. Lampoglou’s work is notable for its practical, industry-oriented approach, bridging the gap between advanced deep learning models and real-world manufacturing needs. With her growing citation record, she is establishing herself as a key voice in the application of computer vision to materials inspection and robotic automation.
Research Focus
Key Achievements
Top Papers
- 1