Papers
2
Total Citations
92
H-Index
2
About
Xinke Li is a researcher whose work bridges optical engineering, structural health monitoring, and intelligent systems. His key research areas include non-destructive testing, computer vision, and AI-driven recommendation systems. Li’s most notable contribution is in cable surface damage detection for cable-stayed bridges, where he pioneered the use of optical techniques and image mosaicking—a method that has garnered 65 citations and is widely referenced in infrastructure maintenance studies. This work demonstrates his ability to apply advanced imaging to solve real-world engineering challenges. Additionally, Li has explored the application of intelligent recommendation techniques for consumer food choices in restaurants, a paper with 27 citations that addresses the growing integration of robotics and AI in food service. By combining computer vision with recommendation algorithms, he contributes to making robotic servers more context-aware and customer-friendly. Li’s research stands out for its practical impact, spanning from critical infrastructure safety to everyday consumer experiences, and his work continues to influence both academic and industrial applications in smart systems and structural diagnostics.
Research Focus
Key Achievements
Top Papers
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