Case study of concealed weapons detection at stand-off distances using a compact, large field-of-view THz camera
Bibliographic record
Abstract
The detection of concealed weapons in crowd situations is a critical need and solutions are being sought after by security agencies at the federal, state and municipal levels. Millimeter waves have been evaluated for these kinds of applications, but the currently available technologies are typically too large and bulky to allow for widespread deployment. Alternatively soft X-rays have been considered but safety issues hinder their acceptance. Terahertz technology is ideally suited for such an application as it has the ability to see through clothing, and offers higher resolution than in the millimeter band, also being more compact. THz photons have lower energy than infrared and do not show the ionizing properties of X-ray radiation. The longer Terahertz waves penetrate deeper into various materials then their visible and infrared counterparts. Though the wavelength is longer it has been shown that high resolution in a small form factor can be obtained in the THz wavebands thanks to the use of small pixel pitch detectors. In this paper, a case study for the use of a compact THz camera for active see-through imaging at stand-off distances is presented. More specifically, the cases of seeing through packages and clothing are analyzed in the perspective of concealed weapons detection. The paper starts with a review of the characteristics of a high resolution THz camera exhibiting small pixel size and large field-of-view. Some laboratory results of concealed object imaging along with details of a concept for live surveillance using a compact see-through imaging system are reviewed.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".