THERMAL INFRARED INSPECTION OF ROOF INSULATION USING UNMANNED AERIAL VEHICLES
Bibliographic record
Abstract
Abstract. UAVs equipped with high-resolution thermal cameras provide an excellent investigative tool used for a multitude of building-specific applications, including roof insulation inspection. We have presented in this study a relative thermographic calibration algorithm and a superpixel Markov Random Field model to address problems in thermal infrared inspection of roof insulation using UAVs. The relative thermographic radiometric calibration algorithm is designed to address the autogain problem of the thermal camera. Results show the algorithm can enhance the contrast between warm and cool areas on the roof surface in thermal images, and produces more constant thermal signatures of different roof insulations or surfaces, which could facilitate both visual interpretation and computer-based thermal anomaly detection. An automatic thermal anomaly detection algorithm based on superpixel Markov Random Field is proposed, which is more computationally efficient than pixel based MRF, and can potentially improve the production throughput capacity and increase the detection accuracy for thermal anomaly detection. Experimental results show the effectiveness of the proposed method.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".