Date of Award
5-2026
Degree Type
Masters Thesis
Degree Name
Master of Science (MS)
School
Computing Sciences and Computer Engineering
Committee Chair
Dr. Rabab Abdelfattah
Committee Chair School
Computing Sciences and Computer Engineering
Committee Member 2
Dr. Sarah Lee
Committee Member 2 School
Computing Sciences and Computer Engineering
Committee Member 3
Dr. Ahmed Sherif
Committee Member 3 School
Computing Sciences and Computer Engineering
Abstract
Crack detection and segmentation are fundamental tasks in structural health monitoring, enabling early identification of damage in critical infrastructure such as roads, bridges, and buildings. While deep learning models—particularly convolutional encoder--decoder architectures—have achieved high accuracy in pixel-level crack segmentation, their deployment in real-world environments introduces significant reliability challenges. In practical scenarios, especially in UAV-based inspection, visual conditions such as illumination variation, motion blur, low resolution, and occlusions can severely degrade segmentation performance. Moreover, the absence of ground-truth annotations during deployment makes conventional evaluation metrics, such as Intersection-over-Union and Dice score, inapplicable, creating a critical gap between model performance and operational trust.
This thesis reframes crack segmentation from a purely accuracy-driven task to a reliability-centered problem. First, it provides a comprehensive analysis of crack segmentation methods, highlighting limitations in thin-structure preservation, generalization, and robustness under real-world conditions. Building on these insights, the thesis introduces a novel semantic monitoring framework based on the LLM-as-Judge paradigm. In this framework, a lightweight crack segmentation model operates onboard a UAV, while a multimodal large language model evaluates segmentation outputs using visual reasoning, producing a quality score, confidence estimate, and explanatory feedback without requiring ground-truth annotations.
To ensure trustworthiness, a rigorous evaluation methodology is proposed, defining repeatability and sensitivity as key reliability criteria. Extensive experiments under controlled perturbations demonstrate that the proposed framework achieves stable, consistent, and perceptually meaningful evaluations. This work establishes a new direction for crack segmentation by enabling reliable, interpretable, and deployment-ready assessment in safety-critical environments.
ORCID ID
0009-0000-1699-5493
Copyright
© 2026 Murad Hasan
Recommended Citation
Hasan, Murad, "LLM-as-Judge for Reliable Crack Segmentation in Edge-Based Structural Inspection" (2026). Master's Theses. 1196.
https://aquila.usm.edu/masters_theses/1196