Few-Shot Learning Augmented with Image Transformation for Multiclass Structural Damage Classification
Bibliographic record
Abstract
The application of machine learning (ML) as an apparatus for structural health monitoring (SHM) has become increasingly prevalent recently as the domain moves toward autonomous structural inspections. Although significant work has been conducted to integrate ML in SHM, many domain-specific issues adopting these technologies are still prevalent. For instance, ML is characterized as a data-intensive technique, requiring a significant number of samples to properly train a new model which are often unavailable in SHM applications. Furthermore, the generalization of these models to new categories of damages and structural and material types results in inferior damage classification. Therefore, to address the scarcity of data within SHM, few-shot learning (FSL) models, such as prototypical networks, have been recently explored as they are capable of training accurate classification models with limited images. However, the use of limited data results in model overfitting and may not adapt well to novel classes of data originating from new material and structural sources. In this paper, the effect of several image transformation techniques on the performance of a prototypical network is investigated concerning surface-level damages for concrete and asphalt structures. The effects of intramaterial data sets (data sets derived from the same material type), and intermaterial data sets (data sets derived from different material types) are investigated to understand and quantify the domain adaptation of these models. It was demonstrated that for k>2, histogram equalization, logarithmic transform, and power transform performed marginally better (1%–5% for both material scenarios) than standard grayscale images when training the chosen prototypical network. The use of phase stretch transform and histogram equalization provided a better reduction to overfitting for both material scenarios (1%–5% and 1%–3%, respectively) when compared to grayscale, further demonstrating the effectiveness of image transformation techniques for reducing the overfitting problem of FSL models.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".