Image Processing to Extract Ice Features to Aid Modelling of Ice Force
Bibliographic record
Abstract
Abstract Sea-ice observation and estimation of ice forces are becoming increasingly important with the increased activities in arctic water. Proper modelling of ice properties and forces is crucial in such cases to ensure safe operations, for example, Dynamic Positioning (DP). This work, therefore, aims at developing algorithms for image processing to extract useful ice properties and subsequently aid the modelling of ice load exerted on a floating platform or a ship. A robust algorithm capable of detecting closely connected and overlapped ice floes with various sizes and shapes is presented, which is the first step for accurate modelling of ice forces. To demonstrate the effectiveness of this approach, image frames processed from videos produced during an experiment using a model ship performed at the National Research Council’s Ocean Coastal and River Engineering Research Centre (NRC-OCRE) in Canada are used. Simulated ice floe images are also used to show the efficiency of the proposed model and compare it with other published work. The model will be extended further to extract and correlate other ice properties with the ice forces and develop a machine learning based ice force prediction model. The predicted force from that model will then be used as a feedforward to Dynamic Positioning (DP) controller with an aim to improve the performance of the controller.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".