Development of a test bench for the assessment of digital processing efficiency in thermal imaging systems
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.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".