Heat loss detection using thermal imaging by a small UAV prototype
Bibliographic record
Abstract
There is an increasing interest in detecting heat loss through buildings using unmanned aerial vehicles (UAVs) and thermal sensors. The present study constitutes an attempt to develop a system that can detect heat loss in different inaccessible portions of building structures, such as roofs and high-rise facades. Traditionally, inspectors have conducted surveys to investigate insulation performance and detect heat loss through various portions of buildings. However, these kinds of surveys tend to be time-consuming, costly, and risky. To mitigate risks, a small, low-cost Adafruit thermal infrared sensor and a small, onboard Raspberry Pi microcontroller were mounted on a UAV to detect heat loss through buildings. The lightweight Raspberry Pi microcontroller and Adafruit thermal sensor were powered by additional batteries. A lightweight battery was selected based on the maximum payload and power demand of the microcontroller and thermal sensor. The Raspberry Pi was controlled remotely by a portable computer. The UAV flight plan was controlled remotely by FreeFlight Pro software. Several experimental tests were conducted in both indoor and outdoor environments. Both video and image data were obtained remotely from the thermal sensor and microcontroller. A standard FLIR thermal camera with a very high resolution was also used to ensure the accuracy of the results obtained from the UAV-based thermal sensor. All the images captured by the Adafruit thermal sensor were compared with the standard thermal camera images. The results showed that the presently developed system can detect heat loss through inaccessible locations in buildings with modifications only in sensor resolution.
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.
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) | 0.000 | 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 | 0.001 | 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".