Papers
7
Total Citations
95
H-Index
5
About
Ruwen Qin is a leading researcher at the intersection of civil infrastructure inspection, robotics, and artificial intelligence. Her work focuses on developing intelligent systems that automate the analysis of inspection data for aging bridges and power grids, addressing critical challenges in transportation and energy infrastructure maintenance. Qin’s major contributions include pioneering semi-supervised and multitask deep learning models that parse bridge elements and segment defects from inspection videos and images—work that has garnered over 20 citations per paper. Notably, her 2021 study on a semi-supervised self-training method for multiclass bridge element segmentation (25 citations) and her 2020 modeling of a robotic bridge inspection system (22 citations) have advanced the automation of condition assessment, reducing reliance on costly manual inspections. She has also explored routing algorithms for suspended robots inspecting grid transmission systems, enhancing cost-effective and energy-efficient maintenance. With a growing body of work cited over 90 times, Qin’s research is instrumental in keeping human expertise in the loop while leveraging drones and robots for safer, faster infrastructure evaluation.
Research Focus
Key Achievements
Top Papers
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- 2Modeling and Simulation of a Robotic Bridge Inspection System22 citations · 2020
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- 6Cost Analysis for High Voltage Transmission Line Inspection using Robot2 citations · 2017
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