Papers
1
Total Citations
3
H-Index
1
About
Xianyong Li is a researcher at the forefront of intelligent inspection and defect detection for large-scale hydropower infrastructure. His work centers on applying advanced computer vision and deep learning to solve critical problems in industrial safety and maintenance. Li’s most notable contribution is the development of an improved real-time lightweight network for detecting surface defects—such as cavitation erosion, wear, and thermal stress cracks—on the runner blades of large hydraulic turbines. This method, detailed in his 2021 paper, leverages mobile robots for data acquisition, addressing the urgent challenge of inspecting hard-to-reach foundation pits in hydropower units. While his work has garnered early citations, its practical significance lies in enabling faster, safer, and more accurate inspections, directly enhancing the reliability of renewable energy generation. Li’s research bridges the gap between cutting-edge AI and real-world engineering demands, positioning him as a key innovator in industrial defect detection. His efforts exemplify how lightweight neural networks can be tailored for resource-constrained environments, promising broad applications in predictive maintenance and infrastructure monitoring.
Research Focus
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Top Papers
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