YOLO-SD: A Real-Time Crew Safety Detection and Early Warning Approach
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
Wearing safety rope while working at the loft and over the side of a ship is an effective means to protect seafarers from accidents. However, there are no active and effective monitoring methods on ships to control this issue. In this article, a one-stage system is proposed to automatically monitor whether the crew is wearing safety ropes. When the system detects that a crew enters the work area without a safety rope, it will warn the supervisor. In this regard, a safety rope wearing detection dataset is established. Then a data augmentation algorithm and a boundary loss function are designed to improve the training effect and the convergence speed. Furthermore, features from different scales are extracted to get the final detection results. The obtained results demonstrate that the proposed approach YOLO-SD is effective at different on-site conditions and can achieve high precision (97.4%), recall rate (91.4%), and mAP (91.5%) while ensuring real-time performance (38.31 FPS on average).
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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 | 0.000 |
| 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