Guidong Yang
Papers
4
Total Citations
146
H-Index
3
About
Guidong Yang is a leading researcher at the intersection of civil infrastructure inspection, deep learning, and robotic vision. His work focuses on revolutionizing how we maintain and monitor critical infrastructure through automated visual inspection systems. Yang's most impactful contribution is his comprehensive review and algorithm comparison for crack classification, segmentation, and detection (88 citations), which has become a foundational resource for researchers developing autonomous inspection technologies. He further advanced this field by creating high-resolution infrastructure defect detection datasets validated with deep learning (35 citations), addressing the critical need for quality training data in real-world applications. Yang also developed WaterFormer, a novel global-local transformer architecture for underwater image enhancement (21 citations), demonstrating his ability to tackle challenging environmental conditions that hinder robotic vision. His work on multiple-task linear temporal logic path planning showcases his broader interest in enabling robots to execute complex, sequential tasks autonomously. With over 146 citations across his key publications, Yang is establishing himself as a vital contributor to the practical deployment of AI-driven inspection systems that promise to make infrastructure maintenance safer, faster, and more reliable.
Research Focus
Key Achievements
Top Papers
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