Ship Target Detection Algorithm Based on Improved YOLOv3 for Maritime Image
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Accurate identification of ships is the key technology of intelligent transportation in water. At the same time, it also provides a judgment basis for water traffic safety control. This paper proposed a detection method of ships in water based on improved You Only Look Once version 3 (YOLOv3), which is called Feature Attention, Feature Enhancement YOLOv3 (AE-YOLOv3). The feature attention module was constructed by introducing the attention mechanism, which was embedded in Darknet-53 for feature recalibration, which improved the feature extraction ability of the model in the complex navigable background. For the problem of insufficient semantic information of low-level features in the feature fusion process, a feature enhancement module was constructed and applied to the feature fusion part to enhance the receptive field size of the corresponding feature layer and the correlation degree of feature extraction network. Experiments were carried out on the public SeaShips dataset. Experiments show that the detection accuracy is as high as 98.72%, which is better than that of other mainstream ship identification models, fully verifying the superiority of this method in the detection of waterborne traffic ships.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it