A Fine-Grained River Ice Semantic Segmentation based on Attentive Features and Enhancing Feature Fusion
Bibliographic record
Abstract
The semantic segmentation of frazil ice and anchor ice is of great significance for river management, ship navigation, and ice hazard forecasting in cold regions. Especially, distinguishing frazil ice from sediment-carrying anchor ice can increase the estimation accuracy of the sediment transportation capacity of the river. Although the river ice semantic segmentation methods based on deep learning has achieved great prediction accuracy, there is still the problem of insufficient feature extraction. To address this problem, we proposed a Fine-Grained River Ice Semantic Segmentation (FGRIS) based on attentive features and enhancing feature fusion to deal with these challenges. First, we propose a Dual-Attention Mechanism (DAM) method, which uses a combination of channel attention features and position attention features to extract more comprehensive semantic features. Then, we proposed a novel Branch Feature Fusion (BFF) module to bridge the semantic feature gap between high-level feature semantic features and low-level semantic features, which is robust to different scales. Experimental results conducted on Alberta River Ice Segmentation Dataset demonstrate the superiority of the proposed method.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".