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 Mystery Minis 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 Multi-Tool 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).