Image based ice-field characterization and load prediction in managed ice field
Bibliographic record
Abstract
Accurate modelling of ice properties and ice-structure interaction forces is important for operations of ships and offshore platforms in ice-infested water. Extraction of ice features from real-time videos and images can significantly improve ice force prediction. However, accurate extraction of ice floe information is challenging due to several inherent complexities in ice images. This paper presents an ice image processing technique which can extract useful ice properties from a closely connected, unevenly illuminated floe field (with various floe sizes and shapes) with higher precision, compared to similar existing models. Several image processing features, including histogram equalization, wavelet denoising, gradient flow vector, snake algorithm, and distance transformation were applied for extracting ice features. The effectiveness of the proposed method is demonstrated through the processing of simulated and managed ice field images from ice tank, and its performance is compared with two other existing models. The new model detected the total number of floes with more than 80 % accuracy and ice concentration at 95 % and above accuracy for ice basin test images. It is also nearly 50 % faster compared to the previous model. The extracted ice features' information is then used to train and test two separate force predictors based on Support Vector Machine (SVM) and Feedforward Neural Network (FFNN). This work is a first step towards developing an image-based force prediction tool from real-life ice field.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".