A proof-of-concept optical tripwire detector
Bibliographic record
Abstract
There are currently no fielded technologies for noncontact detection of tripwires. Itres Research Ltd. and DRDC Suffield have been conducting research on optical detection of tripwires since 1996, both for hand-held and vehicle-mounted roles. A proof-of-concept brassboard imager, initially for a vehicle-mounted role, has been constructed. The imager uses a high spatial resolution, panchromatic focal plane array whose high degree of integration includes on-board digitization and flexible addressing capabilities for windowing and subimaging. Command, control and signal processing are accomplished by a computer, based on dual 1GHz Pentium III processors. Using a high level, rapid prototyping language, 1 image frame can be processed in 3 seconds. Straightforward improvements should allow true real-time operation to be achieved. Preliminary testing of the imager was conducted in the outdoor DRDC Suffield Mine Pen in January 2003. Taut, sagging and undulating tripwires of various materials were partially hidden, often nearly invisible to the naked eye, in a number of types of local vegetation. Preliminary, quasi-real-time results showed that many of the wires were detected, although a significant number of false alarms occurred. As expected with the present algorithm, sagging, undulating and highly obscured wires were often difficult to detect. The instrument, results of the trial, planned improvements and future research will be discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".