Infrared Image Analysis for Estimation of Ice Load on Structures
Bibliographic record
Abstract
Abstract An analysis using infrared and visual images is made to measure the ice thickness of a cylindrical component. The proposed method is useful for ice detection and measurement on structures, even in harsh conditions and low light situations such as night. This type of analysis can fill a gap of knowledge related to ice measurement using both visual and thermal images. Thermal imaging shows differences in the emissivity and temperature of objects. This can help to detect objects and measure the amount of ice accumulated on the objects. Combining the information of visual and thermal images can compensate for their weak points and present better results. Combinations of the color-visual image (CVI), grayscale-visual image (GVI), color-infrared image (CII) and grayscale-infrared image (GII) are used to find the most accurate results. A binary image is acquired using the threshold method based on data collected from infrared and visual images. Using threshold levels removes irrelevant data that come from the background. Common ice pixels detected from both infrared and visual images are considered as the ice area. Thresholding methods cause unwanted gaps and strips in binary images. Morphological algorithms are used to remove these imperfections. The best results are obtained when one of the elements of the combinations is CII. The results of using CVI and GVI are almost the same. The experiments show that this method is reliable and its results are aligned with the real data.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".