Weld defects present within pipelines constitute a considerable threat for leakage and rupture accidents. To elevate the detection precision of these defects, X-ray inspection was employed as a means to identify and locate them with greater accuracy. However, the diverse types, small sizes, and complex backgrounds of weld defects posed challenges for accurate detection. To address the limitations of current deep learning-based models, such as inadequate adaptability to complex backgrounds and lighting variations, as well as poor performance in detecting small targets, an improved faster region convolutional neural networks(Faster R-CNN) network model was investigated. This model incorporated a channel attention mechanism into the backbone network, modified the residual block structure, and employed ROI Align to replace the traditional ROI Pooling. The results show that compared to the original algorithm, the improved Faster R-CNN model achieves significant improvements in mean average precision (mAP) and F₁, with respective increases of 15.82% and 16.44%. It is concluded that this improved model can meet the high-precision requirements for weld defect detection and holds significant theoretical importance as well as promising prospects for engineering applications.