Carl James Debono
Papers
7
Total Citations
140
H-Index
6
About
Carl James Debono is a leading researcher at the intersection of computer vision, robotics, and structural health monitoring. His work focuses on automating the inspection of critical infrastructure—tunnels, bridges, and industrial facilities—using vision-based systems and robotic platforms. Debono’s most influential contribution is a vision-based change detection method for inspecting tunnel liners, which has garnered 64 citations and addresses the challenge of detecting subtle structural defects over time. He has also pioneered complete-coverage path planning for climbing robots inspecting cable-stayed bridge towers, integrating building information models to enable autonomous navigation. His research extends to quantitative crack assessment using active-learning-integrated transformers deployed on unmanned robotic platforms (15 citations), and a comprehensive virtual reality system for tunnel documentation (13 citations). Debono’s work on image mosaicing for tunnel wall images and RGB-D video-based wire detection for robotic arm alignment further demonstrates his versatility. By replacing subjective, hazardous manual inspections with objective, automated systems, his research significantly enhances safety and efficiency in infrastructure monitoring, with over 140 total citations across his most-cited works.
Research Focus
Key Achievements
Top Papers
- 1Vision-based change detection for inspection of tunnel liners64 citations · 2018
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- 6Image mosaicing of tunnel wall images using high level features8 citations · 2017
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