Texture analysis in corrosion management: A scoping review
Bibliographic record
Abstract
Corrosion-induced failures result in economic losses exceeding 3-4% of GDP annually across developed nations, necessitating advanced detection and monitoring methodologies. Texture analysis techniques have emerged as powerful tools for automated corrosion assessment, evolving from traditional statistical descriptors to sophisticated deep learning approaches. This scoping review systematically maps the landscape of texture analysis methodologies applied to corrosion detection, monitoring, and management across industrial sectors, identifying current capabilities, limitations, and research gaps. Following PRISMA-ScR guidelines, a comprehensive search across IEEE Xplore, ScienceDirect, Scopus, SpringerLink, and ACM Digital Library for literature published between 2010-2025 was conducted. Search terms encompassed texture analysis methods (GLCM, LBP, HOG, wavelet transforms, CNN-based approaches) combined with corrosion-related keywords. A total of 127 relevant studies were identified, spanning traditional texture descriptors, hybrid approaches, and deep learning methods, which was further filtered down to 25 representative studies. Performance metrics ranged from 78-98% accuracy, with CNN-based methods showing better performance in complex industrial environments. Traditional texture analysis methods such as GLCM and LBP continue to perform adequately in controlled settings but fall short in complex industrial scenarios compared to CNN-based approaches. Hybrid methodologies that blend traditional texture descriptors with deep learning show promise by balancing accuracy and computational efficiency.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".