Ramps are crucial to offshore platforms, and their absence will cause great safety risks to operation sites. To eliminate such risks, this study proposes a detection method of ramp setting up in the berthing row scenario. The method is divided into three parts: firstly, using the object detection algorithm to locate and mark the target; then, extracting the external edge of the marked target area by edge detection; finally, formulating the specific safety measures discrimination algorithm to identify violations in the work site. To solve the detection problems of small targets, this method improves the YOLOv5 and introduces an attention module in feature extraction and feature fusion, which makes the model more lightweight while improving its mean average precision (mAP) from 53.1% to 54.5%. As to rough edge detection, the loss function of the edge detection network PiDiNet is improved. Compared with the original network, the false detection rate decreases from 8.9% to 5.4%. The verification results indicate that the method can be used to detect whether the ramp is set up correctly within the effective time with accuracy up to 91.5%.