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

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).