Geometric Parameter Evaluation of Oil and Gas Pipeline Defects Based on Deep Learning Neural Networks

Pipeline magnetic flux leakage (MFL) detection technology has been widely used in online pipeline defect detection due to its advantages of not requiring coupling agents and being easily automated. Accurately predicting defect sizes based on detection data and conducting applicability evaluations are crucial for subsequent repair decisions. Based on the collected MFL data, a deep learning object detection model based on PP-YOLOE (Paddle Paddle - You Only Look Once Evolved) is proposed according to the characteristics of defect data. By converting MFL data into images and feeding them into the model for object detection training, defects can be quickly located and data extracted, providing an accurate and reliable dataset for defect quantification. The axial and radial MFL data of pipeline defects are used as inputs to the model, and the length, width, and depth of defects are output in parallel according to the characteristics of MFL data, thus achieving the assessment and prediction of defect sizes.