YOLO-SD: A Real-Time Crew Safety Detection and Early Warning Approach

YOLO-SD: A Real-Time Crew Safety Detection and Early Warning Approach

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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 goldwell topchic 7n@bp 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 8 oz red bull can dimensions 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).