The global reliance on oil and gas pipelines for energy transportation is increasing. As the pioneering review in the field of ultrasonic defect detection for oil and gas pipelines based on bibliometric methods, this study employs visual analysis to identify the most influential countries, academic institutions, and journals in this domain. Through cluster analysis, it determines the primary trends, research hotspots, and future directions in this critical field. Starting from the current global industrial ultrasonic in-line inspection (ILI) detection level, this paper provides a flowchart for selecting detection methods and a table for defect comparison, detailing the comparative performance limits of different detection devices. It offers a comprehensive perspective on the latest ultrasonic pipeline detection technology from laboratory experiments to industrial practice.

This study developed the Deformable Large-kernel Context-fused Spatial (DLCS)-YOLO model to address various challenges involved in permanent magnetic field perturbation (PMFP)-based defect detection for long-distance oil and gas pipelines, including a high false-positive rate, susceptibility to background noise interference, difficulty in identifying small-scale defects, low precision in feature representation and defect type discrimination, and poor adaptability to multiscale defects. The proposed model is an improved version of You Only Look Once (YOLO) v11n. The backbone of the proposed model contains the C3k2-Deformable Attention (C3k2-DAttention) module and the Spatial Pyramid Pooling-Fast-Large Separable Kernel Attention (SPPF-LSKA) module, which is used in place of the SPPF module to enhance robustness to noise and fine-grained feature extraction for small-scale defects. In the feature fusion layer, the Context-Guided Feature Pyramid Network (Context-Guided FPN) module is used to replace the conventional concatenation operation, thereby improving feature representation and defect classification accuracy. Furthermore, the Spatially Enhanced Attention Module (SEAM) is incorporated into the detection head to enhance adaptability in complex scenarios, including those involving background interference and multiscale defects. Experimental results indicate that the proposed model achieves mAP@50 and mAP@50:95 values of 94.5% and 64.7%, respectively, on a self-constructed dataset, with a computational cost of only 6.2 GFLOPs. Compared with the baseline YOLOv11n model, the proposed model exhibits a 3.1% higher precision, a 3.8% higher mAP@50 value, and a 3.0% higher mAP@50:95 value and requires 0.1 fewer GFLOPs. The proposed algorithm effectively enhances the accuracy and efficiency of pipeline defect detection, demonstrating considerable practical value and broad application prospects for detecting defects in oil and gas pipelines.