A U-Net Convolutional Neural Network Deep Learning Model Application for Identification of Energy Loss of Infrared Thermographic Exterior Building Envelope Images
Why this work is in the frame
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Bibliographic record
Abstract
<p>This study presents a novel U-NET convolution neural network (CNN) deep learning (DL) model, developed in a Python environment for the identification of envelope deficiencies on a data set of infrared (IR) thermographic images of building envelopes. A data set of 142 IR images acquired with an unmanned aerial vehicle (UAV) were used with supplementary segmentation masks created for appropriate U-NET modelling application. This data preparation process is presented followed by an in-depth review of the CNN architecture used for the segmentation process. The Python3 code developed for this thesis is reviewed in depth, for an easy application in future work performed by non-data-science researchers. The results of this research presented roughly 43% accuracy and very promising novel outputs from the analytical system. The available data used for this study was noted to be the key limitation with this research.</p>
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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