Multi‐granularity feature enhancement network for maritime ship detection
Bibliographic record
Abstract
Abstract Due to the characteristics of high resolution and rich texture information, visible light images are widely used for maritime ship detection. However, these images are susceptible to sea fog and ships of different sizes, which can result in missed detections and false alarms, ultimately resulting in lower detection accuracy. To address these issues, a novel multi‐granularity feature enhancement network, MFENet, which includes a three‐way dehazing module (3WDM) and a multi‐granularity feature enhancement module (MFEM) is proposed. The 3WDM eliminates sea fog interference by using an image clarity automatic classification algorithm based on three‐way decisions and FFA‐Net to obtain clear image samples. Additionally, the MFEM improves the accuracy of detecting ships of different sizes by utilising an improved super‐resolution reconstruction convolutional neural network to enhance the resolution and semantic representation capability of the feature maps from YOLOv7. Experimental results demonstrate that MFENet surpasses the other 15 competing models in terms of the mean Average Precision metric on two benchmark datasets, achieving 96.28% on the McShips dataset and 97.71% on the SeaShips dataset.
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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) | 0.001 | 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".