An intelligent method for identifying magnetic flux leakage defects in small-diameter pipe elbows based on deep learning

The elbow portion of a pipeline plays a critical role in pipeline systems, and defects can lead to potential gas and oil leakage accidents. Magnetic flux leakage (MFL) detection is an efficient method for identifying pipeline defects. To address the issue of image distortion in defect MFL signals, the accuracy of defect recognition must be improved. This paper proposes an intelligent identification method for MFL defects in small-diameter pipe elbows based on a deep learning target detection algorithm. The MSRCR algorithm is enhanced using bilateral filtering and gamma correction to improve the image features of defect MFL signals. Additionally, the YOLOv5 network is augmented with CBAM, Soft-NMS and Focal-EIOU loss to enhance the feature extraction capabilities. The results show that the proposed improved MSRCR algorithm effectively solves the elbow MFL defect distortion problem. The improved YOLOv5 network achieves average defect recognition accuracies of 86.61% and 94.27% on the original and enhanced datasets, respectively. After experimental verification, the network effectively improves the recognition accuracy of MFL defects in small-diameter pipe elbows, and provides useful technical support for the intelligent detection and safety evaluation of elbows.