Application of EfficientNet and YOLOv5 Model in Submarine Pipeline Inspection and a New Decision-Making System

Abstract

Submarine pipelines are the main means of transporting oil and gas produced offshore. The present work proposed a deep learning technology to identify damage caused by characteristic events and abnormal events using pipeline images collected by remotely operated vehicles (ROVs). The EfficientNet and You Only Look Once (YOLO) models were used in this study to classify images and detect events. The results show that the EfficentNet model achieved the highest classification accuracy at 93.57 percent, along with a recall rate of 88.57 percent. The combining of the EfficentNet and YOLOv5 models achieved a higher accuracy of detecting submarine pipeline events and outperformed any other methods. A new decision-making system that integrates the operation and maintenance of the model is proposed and a convenient operation is realized, which provides a new construction method for the rapid inspection of submarine pipelines. Overall, the results of this study show that images acquired via ROVs can be applied to deep learning models to examine submarine pipeline events. The deep learning model is at the core of establishing an effective decision support system for submarine pipeline inspection and the overall application framework lays the foundation for practical application.