Improving the Accuracy of Temperature Distribution Images Using an Infrared Camera with SLAM
Bibliographic record
Abstract
建物の構造体には，スケルトン保護や美観を目的として，タイルやモルタル工事などの外装仕上げが施されている．しかしながら，経年劣化により剥離したコンクリートやタイルなどの一部が落下して，重大な事故を引き起こす可能性がある．これを防ぐために、外観の目視検査や打音法による検査が行われているが，足場が必要であることや検査員の技量が診断精度に影響することなど，多くの問題がある．そこで，遠隔で検査ができるパッシブ赤外線サーモグラフィ法が使用されている．本手法では，太陽光や周囲の建物の背景反射成分により測定精度が低下するという問題がある．本研究では，SLAMによって位置推定を行い，方向を変えて撮影した赤外線画像を対象平面に逆射影することで反射低減を行う手法を開発した．
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".