Surface moisture detection using thermal imaging and computer vision
Bibliographic record
Abstract
Thermal imaging is used to detect moisture inside surfaces such as walls or floors by showing the temperature difference between the moisture and the structure. Surface moisture detection can be critical in quality assurance, healthcare, construction and agriculture. This paper aims to extend the usage of thermal imaging and computer vision to detect the coverage of moisture on the surface using computer vision rather than relying on an end user. This process relies on the thermal properties of the liquid that is sprayed on a surface, which would have a distinct temperature difference compared to the surface it is on. The methodology proposed in this paper is to utilize an infrared thermal image camera to analyze the surface. Then, using computer vision, the output is processed to detect the areas of the largest temperature gradients while filtering the noise. This ensures only areas with a large enough gradient are highlighted, capturing the sprayed surface. These areas are converted to a percentage of the captured area and displayed to the user. Preliminary findings from the experiments show that the system is able to detect liquids that have a temperature difference of at least 5 deg C (9 deg F). As this method only relies on thermal imaging, it is a non-destructive and non-invasive test, where the user does not need to interact with the surface or the liquid directly. The information provided by the technology can contribute to fault detection and quality control when it comes to spray coverage.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".