Hiran Ganegedara
Papers
1
Total Citations
8
H-Index
1
About
Hiran Ganegedara is a researcher whose work sits at the intersection of computer vision, automation, and infrastructure management. His primary research focuses on developing intelligent systems for automated defect identification in large-scale image collections, with a particular emphasis on sewer pipe inspection. His most cited work, "Self organising map based region of interest labelling for automated defect identification in large sewer pipe image collections" (2012, 8 citations), introduces a novel approach that leverages self-organizing maps to label regions of interest, enabling more efficient and objective detection of structural defects. This contribution is significant because it addresses a critical urban maintenance challenge—the difficulty of manually inspecting sewer pipes—by providing a scalable, automated solution. By reducing human error and inspection time, Ganegedara's work has implications for the long-term health and functionality of city infrastructure. His research demonstrates a practical application of machine learning to real-world engineering problems, making it particularly valuable for students and researchers interested in the intersection of AI, civil engineering, and urban sustainability.
Research Focus
Key Achievements
Top Papers
- 1