Reconstructing defect contours by inspecting internal magnetic flux leakage (MFL) in oil and gas pipelines provides significant data support for assessing pipeline integrity and represents a crucial step in MFL data analysis. Traditional methods often suffer from blurred edges and low structural similarity in multiple reconstructed images, making them inadequate for high-precision inspection. This paper proposes a three-dimensional (3-D) pipeline defect contour inversion method based on direction-aware multi-axis fusion and depth-gradient joint decoding (MFL-MAARN), which enhanced feature representation by fusing MFL signals with coordinate information. This work also designed a deep network architecture that combined a residual module with an attention gate mechanism and constructed a composite loss function combining MAE and the structural similarity index (SSIM). This achieved dual optimization aimed at both 3-D defect contour detail recovery and global structural reconstruction. The experimental results indicated that the MFL-MAARN model effectively reproduced the true defect morphology for rectangular, cylindrical, conical, regular, and irregular defects, accurately capturing defect depth information. The model also demonstrated high fitting accuracy for defect reconstruction from actual pipeline MFL internal inspection signals, further validating its generalization ability and robustness. This model provides efficient and accurate technical support for pipeline safety inspection.

Long-distance pipelines are essential for transporting oil and gas, with the quality of welds directly affecting their safety and reliability. Weld defects can emerge during the welding process due to improper techniques or environmental factors, which can result in pipeline leakage or rupture that pose a public safety and environmental risk and can lead to significant economic losses. Therefore, effective weld defect detection is crucial to ensure the safe operation of long-distance pipelines. Although traditional X-ray inspection is commonly used to detect weld defects, it is inefficient and subjective due to its dependence on manual analysis. New developments in computer vision have significantly improved the efficiency and accuracy of automated technology for defect recognition in pipeline weld X-ray images. As a result, there is an urgent need for a comprehensive review to support the development of this field and provide valuable insights. This paper comprehensively evaluates the progress in the technology for the intelligent recognition of defects in pipeline weld X-ray images, focusing on preprocessing and defect detection techniques. This review explores three key directions for intelligent weld defect recognition: signal processing, feature design, and deep learning-based methods. Deep learning-based defect recognition techniques were examined in detail from five primary perspectives: dataset creation, image classification, semantic segmentation, object detection, and performance evaluation. Finally, the challenges and future development trends in the intelligent recognition of defects in pipeline weld X-ray images are discussed, emphasizing areas that require further research and innovative advancements.