A General Multiscale Pyramid Attention Module for Ship Detection in SAR Images
Bibliographic record
Abstract
Compared with large scale ships, small scale ships occupy few pixels and have low contrast, so it poses a great challenge to detect multi-scale ships in SAR images. In order to improve the accuracy of multi-scale ship detection in SAR images, this paper designs a general multi-scale pyramid attention module (MPAM), which is a plug-and-play lightweight module that can adapt to many ship detection networks. In MPAM, a deep feature extraction sub-module (DFES) is first designed to use the multi-scale pyramid structure to divide the feature map into different levels, extracting rich features with resolution and semantic information for multi-scale ship detection. The channel multi-layer attention fusion sub-module (CMAFS) and spatial multi-layer attention fusion sub-module (SMAFS) are then designed to fuse the channel and spatial attention blocks on different level feature maps, which could better learn the dependent features from the channel and spatial dimensions, to enhance the feature representation. Finally, the fused feature map is input into the existing ship detection networks to obtain the detection result. Experiments on SAR datasets containing multi-scale ships show that the effectiveness of MPAM in improving the accuracy of the existing ship detection networks.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".