A Feasibility Study on Translation of RGB Images to Thermal Images: Development of a Machine Learning Algorithm
Bibliographic record
Abstract
Abstract The thermal image is an important source of data in the fire safety research area, as it provides temperature information at pixel-level of a region. The combination of temperature value together with precise location information from thermal image coordinates enables a comprehensive and quantitative analysis of the combustion phenomenon of fire. However, it is not always easy to capture and save suitable thermal images for analysis due to several limitations, such as personnel load, hardware capability, and operating requirements. Therefore, it is necessary to have a substitution solution when thermal images cannot be captured in time. Inspired by the success of previous empirical and theoretical study of deep neural networks from deep learning on image-to-image translation tasks, this paper presents a feasibility study on translating RGB vision images to thermal images by a brand-new model of deep neural network. It is called dual-attention generative adversarial network (DAGAN). DAGAN features attention mechanisms proposed by us, which include both foreground and background attention, to improve the output quality for translation to thermal images. DAGAN was trained and validated by image data from fire tests with a different setup, including room fire tests, single item burning tests and open fire tests. Our investigation is based on qualitative and quantitative results that show that the proposed model is consistently superior to other existing image-to-image translation models on both thermal image patterns quality and pixel-level temperature accuracy, which is close to temperature data extracted from native thermal images. Moreover, the results of the feasibility study also demonstrate that the model could be further developed to assist in the analytics and estimation of more complicated flame and fire scenes based only on RGB vision images.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".