Coordinate Attention-enabled Ship Object Detection with Electro-optical Image
Bibliographic record
Abstract
Shipping safety is one of the factors restricting the development of navigation. In particular, the route near the shore is prone to unknown risks due to the existence of multiple types of ships, the density of ships, the shielding between ships, and other reasons. This paper presents a method for detecting medium-range ships, which can improve security for ships. This method is based on the You Only Look Once Version 5 network (YOLOv5). To improve the accuracy, the coordinate attention model is integrated into the detection network. The main research content and experimental work of this paper are as follows. Firstly, the YOLOv5 network and spatial attention mechanism are analyzed. Then, detection experiments were carried out based on YOLOv5 and Singapore Maritime Data Set (SMD). Then, the coordinate attention model was used to improve the network. Finally, by adjusting training parameters and improving attention, the mAP of test results of the object detection network reaches 73%, and the feasibility of object detection of the YOLOv5 algorithm with coordinate attention is confirmed.
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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 | 0.000 |
| 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 | 0.000 |
| 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".