Yongqiang Lu
Papers
1
Total Citations
13
H-Index
1
About
Yongqiang Lu is a researcher specializing in structural health monitoring and computer vision, with a focus on advancing crack detection and quantification in high-resolution imagery. His most notable contribution, a 2025 framework integrating Mamba architecture with unmanned devices, addresses the persistent challenge of accurately segmenting slender, complex cracks while reducing computational overhead—a critical advancement for real-time infrastructure inspection. This work, already garnering 13 citations, demonstrates his ability to bridge cutting-edge AI with practical engineering needs. Lu’s research directly tackles the limitations of existing methods, which often falter on intricate crack boundaries and struggle with the demands of high-resolution data. By developing efficient, deployable solutions for unmanned aerial and ground vehicles, he is enabling safer, faster, and more reliable structural assessments. His contributions hold significant promise for aging infrastructure management, disaster response, and automated quality control, positioning him as a rising voice in the intersection of deep learning and civil engineering.
Research Focus
Key Achievements
Top Papers
- 1