A Novel Cascaded Deep Learning Model for the Detection and Quantification of Defects in Pipelines via Magnetic Flux Leakage Signals

In this paper, we present a machine learning based quantitative method for the interpretation of signals gathered from non-destructive-testing (NDT) of steel pipelines via a semi-autonomous in-line-inspection (ILI) robot. The robot has a magnetic-flux-leakage (MFL) sensor that produces three axis data for each point of pipeline with specific intervals. Both the robot and the MFL sensor have been developed in-house. The signals collected via MFL sensor are converted into images to be used as input for the proposed defect detection model. We propose a combination of a defect detection model based on SwinYv5 object detection algorithm and a quantification model based on Cross-Residual Convolutional Neural Network (CR-CNN). The detected defect locations are used to extract the Region of Interest (ROI) images of defects that are used as input for the quantification model. In data collection phase, numerous tests have been conducted via a special test mechanism and a custom data augmentation technique has been deployed in order to increase the amount and variety of training data. According to test results, the proposed method is capable of detecting defects with a precision of 98.9% and quantifying them with maximum errors of 1.30, 1.65 and 0.47 mm for length, width and depth respectively.