In harbor underwater threat detection/identification using active imaging
Bibliographic record
Abstract
We present results from trials of the LUCIE 2 (Laser Underwater Camera Image Enhancer) conducted in Halifax Harbor, Nova Scotia, Canada and Esquimalt Harbor, Victoria, British Columbia, Canada. LUCIE 2 is a new compact laser range gated camera (10 inches in diameter, 24 inches in length, and neutrally buoyant in water) originally designed to improve search and recovery operations under eye safe restrictions. The flexibility and eye safety of this second generation LUCIE makes it a tool for improved hull searches and force protection operations when divers are in the water attempting to identify bottom lying objects. The camera is equipped with a full image geo-positioning system. To cover various environmental and targets size conditions, the gate-delay, gate width, polarization and viewing and illuminating angles can be varied as well. We present an analysis on the performance of the system in various water conditions using several target types and a comparison with diver and camera identification. Coincident in-situ optical properties of absorption and scattering were taken to help resolve the environmental information contained in the LUCIE image. Several new capabilities are currently being designed and tested, among them a differential polarization imaging system, a stabilized line of sight system with step-stare capability for high resolution mosaic area coverage, a precision dimensioning system and a diver guided and operated version.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".