Autonomous Heat Loss Detection in Buildings for Efficient Façade Control and Energy Management
Bibliographic record
Abstract
Insulation defects in building envelopes can lead to heat loss regions, resulting in energy wastage, moisture accumulation, and mold formation within the building fabric. In cold climates, where heating demand is high, timely detection of these defective areas is crucial for enhancing energy efficiency and improving occupants' well-being. This study introduces a method that integrates infrared thermography with YOLOv7 deep learning model to automatically identify and measure thermal anomalies in the building envelope. The method was tested on a multi-unit residential building and achieved a mean average precision (mAP@0.5) of 0.81 pinpointing significant heat loss areas. This approach optimizes real-time building inspections, enabling targeted energy-saving interventions while reducing the need for labor-intensive and expert-driven analysis. The study identified 28 thermal anomaly regions on the examined building façades, with the highest heat loss in the south façade, which accounted for 35% of the total detected anomalies. The proposed method offers a practical solution for improving energy management and façade control in buildings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".