Yueyue Liu
Papers
1
Total Citations
10
H-Index
1
About
Yueyue Liu is an emerging researcher specializing in computer vision, deep learning, and structural safety inspection, with a particular focus on automated defect detection in civil infrastructure. Their most notable contribution, *CrackInst*, introduced a real-time instance segmentation framework designed to detect cracks in underwater dam structures — a technically challenging domain where image quality is compromised by turbidity, lighting limitations, and complex underwater environments. By leveraging underwater robotics equipped with cameras, Liu's approach advances beyond conventional semantic segmentation methods, enabling precise, instance-level crack identification critical for dam safety assessment and maintenance decision-making. Published in 2024 and already accumulating 10 citations within a short timeframe, this work signals meaningful early traction within the infrastructure inspection and intelligent perception communities. Liu's research sits at a compelling intersection of practical engineering safety and cutting-edge AI methodology, addressing real-world demands for reliable, automated monitoring of aging water infrastructure. As climate change intensifies pressure on dam systems globally, Liu's contributions position them as a promising voice in the rapidly evolving field of AI-driven structural health monitoring and underwater visual inspection.
Research Focus
Key Achievements
Top Papers
- 1