Transformer‐based framework for accurate segmentation of high‐resolution images in structural health monitoring
Bibliographic record
Abstract
High-resolution image segmentation is essential in structural health monitoring (SHM), enabling accurate detection and quantification of structural components and damages. However, conventional convolutional neural network-based segmentation methods face limitations in real-world deployment, particularly when handling high-resolution images producing low-resolution outputs. This study introduces a novel framework named Refined-Segment Anything Model (R-SAM) to overcome such challenges. R-SAM leverages the state-of-the-art zero-shot SAM to generate unlabeled segmentation masks, subsequently employing the DEtection Transformer model to label the instances. The key feature and contribution of the R-SAM is its refinement module, which improves the accuracy of masks generated by SAM without the need for extensive data annotations and fine-tuning. The effectiveness of the proposed framework was assessed through qualitative and quantitative analyses across diverse case studies, including multiclass segmentation, simultaneous segmentation and tracking, and 3D reconstruction. The results demonstrate that R-SAM outperforms state-of-the-art convolution neural network-based segmentation models with a mean intersection-over-union of 97% and a mean boundary accuracy of 87%. In addition, achieving high coefficients of determination in target-free tracking case studies highlights its versatility in addressing various challenges in SHM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".