## 1 Introduction  
Steel pipe welding technology is widely employed in chemical, petroleum, natural gas, nuclear, and shipbuilding industries. However, limited by factors such as welding processes and the welding characteristics of materials, defects including circular, linear, non-fusion, non-penetration, and crack defects inevitably occur in pipeline welds [1], which critically endanger pipeline transportation safety [2]. To ensure the quality of welded pipelines, X-ray is widely used to detect internal defects in steel pipes. The inspection results serve as a fundamental basis for analyzing weld defects and evaluating weld quality. Traditional manual inspection faces limitations in efficiency and accuracy when handling large-scale detection tasks due to subjective human judgment, equipment variability, and image quality constraints, which often result in false positives and missed detections. Recent advancements in artificial intelligence, particularly deep learning methodologies, have been progressively implemented in pipeline weld defect detection systems, significantly enhancing the automation and intelligence of both defect recognition and spatial localization processes [3,4]. The current object detection methodologies in artificial intelligence primarily fall into two categories: conventional neural network algorithms and deep learning-based Convolutional Neural Networks (CNN). Compared to traditional neural network algorithms, deep learning-based CNN architectures directly accept raw weld images as input and autonomously learn discriminative features of various weld defects through hierarchical representation learning. This paradigm eliminates the need for separate feature extraction, selection, and classification modules, enabling end-to-end intelligent recognition and localization of weld defects, from the original weld images to defect classification and localization [5]. Deep learning-based object detection models are broadly classified into two categories. Single-stage detectors (SSD [6,7], YOLO [8, 9, 10, 11]) perform direct localization and classification through anchor point regression, offering computational efficiency at the cost of reduced accuracy. Conversely, two-stage models represented by Faster-RCNN first generate region proposals and then perform further classification and regression adjustments on these regions. These models achieve higher precision but are more computationally expensive, making them challenging to meet the real-time detection requirements of industrial applications. With the development of deep learning-based object detection models, continuous improvements have been made to models applied to pipeline weld defect detection. The YOLO series models utilize regression principles to directly predict bounding box coordinates and target categories at different positions in the image. Through continuous improvements in each module, these models are progressively optimized in terms of detection speed, accuracy, and parameter efficiency. [PERSON] et al. [12] integrated the Coordinate Attention (CA) mechanism, Scylla Intersection over Union (SloU) loss function, and Flexible Rectified Linear Unit (FReLU) activation function into YOLOv5, significantly enhancing small object detection, spatial sensitivity under low sensitivity conditions, and global optimization capability. [PERSON] et al. [13] also implemented the Squeeze and Excitation (SE) attention mechanism in the backbone of YOLOv5 and replaced the Cross Stage Partial (C3) module with GhostBottleneck, achieving both lightweight characteristics and enhanced accuracy. [PERSON] et al. [14] incorporated Le-HorBlock modules with the CA mechanism and SIoU loss function into YOLOv7, strengthening both feature extraction and representation capabilities, while reducing the flexibility of the loss function, thereby significantly lowering the miss detection rate compared to the original model. Notably, [PERSON] et al.