Surface moisture detection using thermal imaging and computer vision
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 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 it