Novel Performance Evaluation of Thermal Camera Based on VOx Bolometer Focal Plane Array via Analysis of Sigma NETD, Mean NETD, and Roughness Index
Bibliographic record
Abstract
Keywords: roughness index (RI), noise equivalent temperature difference (NETD), full width at half maximum (FWHM), non-uniformity correction (NUC)With recent advancements in thermal imaging, the evaluation of thermal imaging performance has become important.In this study, the thermal-camera performance parameters of roughness index (RI), noise equivalent temperature difference (NETD), and the full width at half maximum (FWHM) of a statistical NETD histogram are investigated and compared by varying the integration times at different operating temperatures for vanadium oxide (VOx)based microbolometer focal plane arrays (FPAs) with the use of the Matlab algorithm platform.The quantitative performance assessment of an uncooled VOx microbolometer-based thermal imager, which was designed and fabricated by researchers from the National Chung-Shan Institute Science of Technology (NCSIST), Taiwan, and the National Optics Institute (INO), Canada, is proposed systematically.Explicitly, the uncompressed video data streams before non-uniformity correction (NUC) using two-point temperature calibration were acquired for integration times of 16.67, 33.33, and 50 ms at three operating temperatures of 10, 15, and 20 °C.The results from the estimations of NETD, FWHM of the NETD histogram, and the RI for the thermal imager are discussed for the imaging performance evaluation in different infrared operation scenarios.We believe that our findings can significantly contribute to the further development of IR imaging technology.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".