Bowen Zheng
Papers
1
Total Citations
15
H-Index
1
About
Bowen Zheng is a researcher whose work sits at the intersection of computer vision and structural health monitoring, with a particular focus on automated infrastructure inspection. His most cited paper, "A lightweight convolutional neural network for automated crack inspection" (2024), has already garnered 15 citations, reflecting the timely relevance of his contributions. Zheng’s major contribution lies in developing efficient, deployable deep learning models that can identify surface cracks in concrete and other materials—critical for early detection of structural failures. By prioritizing lightweight architectures, his work enables real-time analysis on resource-constrained devices like drones or smartphones, bridging the gap between high-accuracy AI and practical field deployment. This approach not only reduces computational costs but also enhances safety monitoring in aging infrastructure. Zheng’s research is particularly notable for its potential to transform routine visual inspections from manual, time-consuming tasks into automated, scalable processes. His achievements underscore a commitment to making AI accessible for civil engineering applications, and his growing citation count signals a rising influence in the field of applied machine learning for non-destructive evaluation.
Research Focus
Key Achievements
Top Papers
- 1A lightweight convolutional neural network for automated crack inspection15 citations · 2024