Date of Award
5-2025
Degree Type
Thesis
Degree Name
M.S.
Degree Program
Computer Science
Department
Computer Science
Major Professor
Dr. Md Tamjidul Hoque
Second Advisor
Dr. Shreya Banerjee
Third Advisor
Dr. Md Meftahul Ferdaus
Fourth Advisor
Dr. Abdul Rahman Alsamman
Abstract
Abstract: Levees serve as critical flood protection structures, but failures due to inadequate maintenance and extreme water pressures have led to devastating events such as Hurricane Katrina. Manual inspections are slow, labor-intensive, and prone to human error, necessitating the development of automated solutions. This study proposes an AI-driven framework for levee inspection utilizing deep learning-based semantic segmentation to detect rutting and enhance the identification of sand boils. To address dataset limitations, high-fidelity synthetic images are generated using DreamBooth for fine-tuning, while ControlNet adds structural constraints to enhance realism and consistency. A semi-automatic convex hull annotation technique enhances labeling efficiency, and ensemble learning strategies further improve segmentation accuracy. The system integrates real-time inference capabilities within a web-based platform, enabling rapid and precise identification of defects. By combining deep learning, synthetic data augmentation, and real-time deployment, this research presents a scalable, innovative solution for automated levee monitoring, addressing key challenges in flood risk management and infrastructure resilience.
Keywords: Levee Inspection, Deep Learning, Semantic Segmentation, Synthetic Data, Real-time Monitoring, Generative AI.
Recommended Citation
Thapa, Padam Jung, "Toward Robust Semantic Segmentation in Levee Infrastructure Monitoring: Enhancing Accuracy with High-Fidelity Synthetic Data and Ensemble Learning" (2025). University of New Orleans Theses and Dissertations. 3231.
https://scholarworks.uno.edu/td/3231
Rights
The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.