Deep Learning-Based Superpixel Texture Analysis for Crack Detection in Multi-Modal Infrastructure Images
Bibliographic record
Abstract
Infrared and visible imaging play crucial roles in non-destructive testing, where accurate defect segmentation and detection are paramount. However, the scarcity of annotated training data or the limited number of data availability often poses a challenge. To address this, we propose an innovative framework tailored to the domain of infrared and visible imaging, integrating segmentation and detection tasks. The proposed approach eliminates the dependency on annotated defect data during training, enabling models to adapt to real-world scenarios with limited annotations. By utilizing super-pixel segmentation and texture analysis, the proposed method enhances the accuracy of defect detection. Concrete structures, globally subjected to aging and degradation, demand constant monitoring for structural health. Traditional manual crack detection methods are labor-intensive, necessitating automated systems. The proposed approach combines deep learning-based super-pixel segmentation with texture analysis, offering a solution for limited-defect-data situations. Utilizing convolutional neural networks (CNNs) for super-pixel segmentation and texture features for defect analysis, the proposed methodology improves the efficiency and accuracy of crack detection, especially in scenarios with limited labeled data or a limited number of data available. Evaluation on public benchmark datasets have validated the effectiveness of the proposed approach in detecting cracks in concrete structures.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
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".