Augmentation of onboard camera data with ship manoeuvrability for tactical navigation support in ice
Bibliographic record
Abstract
Shipping in Canadian Arctic waters involves significant risks, primarily due to potential ice interactions. To address these challenges, various support tools have been developed to enhance safe navigation in ice-prone regions. These include POLARIS, a system which evaluates vessel suitability for specific ice conditions, and onboard cameras, which act as sensors to capture and monitor ice conditions around a vessel. This study examines the effectiveness of these two decision support tools, emphasizing the need to account for operational parameters such as vessel speed and physical characteristics like vessel length when assessing a ship's ability to navigate safely through ice. Image processing techniques, including projective transformation or homography, are applied to convert onboard camera data into a top-down view, enabling augmentation of ship manoeuvrability parameters, specifically the stopping distance and turning circle parameters. Image rescaling is further employed to achieve a true-scale representation of distances within the field of view. Two sample vessel scenarios are analyzed to evaluate their manoeuvrability in a test case involving a 50m diameter ice hazard at 175m directly ahead of the vessel. The results demonstrate the critical role of vessel speed in stopping distance. The results also show the limitations of using onboard cameras for tactical navigational support, as well as highlighting the limits that POLARIS has in terms of accounting for differences in vessels within the same ice class but with different capabilities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".