Hyun‐Jin Bae
Papers
2
Total Citations
95
H-Index
2
About
Hyun-Jin Bae is a leading researcher in the field of structural health monitoring, specializing in the intersection of computer vision, deep learning, and robotics for civil infrastructure inspection. Her primary research focuses on developing automated, intelligent systems to detect and evaluate cracks in bridges and other large-scale structures. Bae’s most impactful contribution is the introduction of the Deep Super Resolution Crack Network (SrcNet), a novel end-to-end deep learning architecture designed to enhance the detectability of cracks in images captured under challenging, real-world conditions. This work, published in 2020 and garnering 93 citations, directly addresses the common problem of motion blur and low resolution in images taken by unmanned robots, significantly improving the reliability of automated visual inspections. In a subsequent 2021 study, Bae expanded this framework by integrating both drone and climbing robot platforms, comparing their respective strengths for accessing high-angle bridge components and developing tailored deep learning networks for each. Her practical, field-validated approach—demonstrated on actual bridges in South Korea—positions her as a key innovator in making infrastructure assessment safer, faster, and more accurate through advanced AI and robotics.
Research Focus
Key Achievements
Top Papers
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