Development of a test bench for the assessment of digital processing efficiency in thermal imaging systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modern thermal imaging systems are widely used because of their broad military and commercial application range. The performance of the first generations of thermal imagers was limited by resolution and thermal sensitivity. Brightness and contrast adjustments were also the crux of the image quality. From a military user perspective, the amount of details and the interpretation of a scene depends, among others, on the experience of the user and on the time available to complete those adjustments. Modern imagers now feature embedded digital processing that can automatically adjust the device parameters in order to optimize the image quality. With the combined improvements in microprocessor power and microfabrication processes, digital processing enhanced the thermal imagers’ performance until they eventually became limited by their ability to react to different operational scenarios. That brings the need for testing the reaction of digital processing in such operational scenarios. Meanwhile, there were no significant modification in testing methodologies and metrics used for the assessment of thermal imagers. In this paper, we present DRDC-Valcartier Research Centre’s efforts to develop a test bench to measure the efficiency of the digital processing embedded in thermal imagers. The purpose of the testing methodology is to provide reliable, repeatable and user-independent metrics. Outputs quantitatively highlight the impact of digital processing for various operational situations and allow the performance of devices to be compared.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it