A Novel Ship Detector based on Attention Mechanism and Upsampling Operator
Bibliographic record
Abstract
As one of the most important means for earth observation, synthetic aperture radar (SAR) ship detection has been playing an increasingly important role recently. However, the accuracy of one-stage detectors is relatively low when ships are densely arranged in SAR images. It means that current detectors are insufficient to meet the application requirements. In this paper, an improved YOLOv5 detector, which is based on the attention mechanism and upsampling operator, is proposed. We adopt the ECA attention mechanism to solve the issue of insufficient feature extraction capability for ship targets. Besides, we propose the CARAFE upsampling operator to enhance the proportion of ship detail information in reconstructed feature maps. Experiments on the SAR ship detection dataset (SSDD) demonstrate that the detection P, R, and mAP metrics have improved by 4.1%, 4.8%, and 2% compared with the original YOLOv5 algorithm.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".