管道内部的焊缝缺陷是导致管道发生泄漏和破裂事故的主要原因，而X射线能够有效地检测到这些缺陷。然而，焊缝缺陷存在种类多、尺寸小和背景复杂等问题，影响检测精度。针对目前基于深度学习的焊缝缺陷检测模型对图像复杂背景和光照变化的适应性不足、小目标检测效果不佳的问题。在快速区域卷积神经网络（faster region convolutional neural networks, Faster R-CNN）网络的主干网络上添加通道注意力机制和对残差块结构进行修改，并采用ROI Align替换传统Faster R-CNN网络的ROI Pooling的改进模型。实验结果表明：改进后的Faster R-CNN网络模型与原算法相比，平均精度值（mean average precision, mAP）和F₁分别比原算法提升了15.82%和16.44%，能够满足焊缝缺陷检测的高精度要求，具有重要的理论意义与良好的工程应用前景。

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.