Enhanced temperature measurement using infrared thermography in dynamic environments through an automated robust detection-tracking approach
Bibliographic record
Abstract
This work presents a robust approach for temperature measurement in dynamic environments, integrating detection and tracking techniques to enhance accuracy. The proposed method utilises deep learning, particularly convolutional neural networks (CNNs), to detect and track objects of interest within infrared thermography images, eliminating the need for unreliable GPS coordinates. CNNs excel at extracting complex patterns and features from dynamic datasets, enabling effective identification of thermal signatures in varying environmental conditions. The method includes an active learning component to iteratively improve detection and tracking performance, adapting to new data and feedback over time. The proposed system undergoes thorough evaluation, initially using a laboratory prototype to test various configurations, including synthetic false positives and missed detections. The system is then deployed in an industrial facility with a large pipeline system, where an autonomous aerial vehicle performs fully automated inspections, including a possible angle-corrected emissivity handling. A mission planning proposal is also introduced to outline the drone flight execution. The approach addresses several challenges, such as navigation inaccuracies, weather variability, image quality, and processing speed, demonstrating its capacity for accurate temperature measurements even in challenging conditions. Rigorous testing confirms the reliability of the method, highlighting its potential for real-world applications in dynamic industrial environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".