Digital image expert system for corrosion analysis of steel transmission structures
Bibliographic record
Abstract
A Corrosion Management Expert System (CMES) was developed to improve inspection and damage assessment of existing steel transmission structures. CMES analyzes digital images of the corroded steel towers to classify corrosion type and severity. By employing an artificial neural network and RGB color model, CMES classifies pixels into a set of predefined colors. Since RGB is an additive color model, it is sensitive to environmental effects such as sunlight, shadows, etc., which will alter the red, green, and blue pixel values and may adversely affect the CMES's ability to accurately recognize the true pixel color and the image. For example, if a shadow covers part of the image, the system will identify the dark, shadowed areas as corroded spots. To improve the system's accuracy and pattern recognition capabilities, a shadow removal algorithm must be integrated with CMES. In this paper, two algorithms are compared for integration with CMES.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".