Yiwei Zhu
Papers
1
Total Citations
13
H-Index
1
About
Yiwei Zhu is a rising researcher in structural health monitoring and computer vision, whose work focuses on advancing automated defect inspection for civil infrastructure. Their most notable contribution is a novel crack detection and quantification framework that integrates Mamba—a state-of-the-art vision architecture—with unmanned devices for high-resolution image analysis. This framework directly addresses the long-standing challenges of accurately segmenting slender, complex crack boundaries while maintaining computational efficiency for large-scale imagery. With 13 citations since its 2025 publication, this work is quickly gaining traction for its practical applicability in real-world structural inspections. Zhu’s research bridges the gap between deep learning efficiency and engineering precision, offering scalable solutions for monitoring bridges, pavements, and buildings. By enabling unmanned aerial vehicles to autonomously detect and quantify structural defects, their work promises to reduce inspection costs and improve safety. As an emerging voice in this interdisciplinary field, Yiwei Zhu is poised to influence both academic research and industry practices in infrastructure resilience.
Research Focus
Key Achievements
Top Papers
- 1