Papers
7
Total Citations
154
H-Index
5
About
Zhongcheng Gui is a leading researcher in robotic inspection and non-destructive evaluation, with a primary focus on airport runway structural health monitoring. His work bridges robotics, computer vision, and ground-penetrating radar (GPR) technology to automate the detection of both surface and subsurface defects. Gui’s most significant contribution is the development of deep learning algorithms for GPR data interpretation, including the GPR-RCNN (80 citations) and MV-GPRNet (24 citations) networks, which enable robots to automatically identify hidden structural flaws in runways. He also pioneered a cloud-edge-terminal robotic architecture (7 citations) for real-time inspection data processing. Beyond GPR-based detection, Gui has advanced surface crack detection with the AggCrack attention model (3 citations) and designed specialized hardware, including a wall-climbing robot with a multi-body flexible permanent magnetic adhesion system (9 citations). His work on automated defect visualization (29 citations) has been instrumental in translating raw sensor data into actionable maintenance insights. With over 150 total citations, Gui’s integrated approach—combining novel robotic platforms, intelligent algorithms, and distributed computing—is setting new standards for autonomous infrastructure inspection, directly enhancing the safety and reliability of critical transportation assets.
Research Focus
Key Achievements
Top Papers
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- 5A cloud-edge-terminal-based robotic system for airport runway inspection7 citations · 2021
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- 7MOBILE PLATFORM FOR HYDRAULIC TURBINE BLADE REPAIR ROBOT2 citations · 2006