Preprocessing of Edge of Light images: towards a quantitative evaluation
Bibliographic record
Abstract
A computer vision inspection system, named Edge of Light TM (EOL), was invented and developed at the Institute for Aerospace Research of the National Research Council Canada. One application of interest is the detection and quantitative measurement of “pillowing” caused by corrosion in the faying surfaces of aircraft fuselage joints. To quantify the hidden corrosion, one approach is to relate the average corrosion of a region to the peak-to-peak amplitude between two diagonally adjacent rivet centers. This raises the requirement for automatically locating the rivet centers. The first step to achieve this is the rivet edge detection. In this study, gradient-based edge detection, local energy based feature extraction, and an adaptive threshold method were employed to identify the edge of rivets, which facilitated the first step in the EOL quantification procedure. Furthermore, the brightness profile is processed by the derivative operation, which locates the pillowing along the scanning direction. The derivative curves present an estimation of the inspected surface.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".