Abstract: In this paper, the target detection network Faster-RCNN was applied to the X-ray image defect detection of ship welds, and the effect of Faster-RCNN in the X-ray weld defect detection was discussed. Aiming at the X-ray weld image in the shipbuilding industry, this paper first used the CLAHE method to preprocess the weld X-ray image, and took the five types of defects with typical characteristics such as pores, cracks, and LOF in the weld as the identification target annotated and enhanced the data. In object detection, ResNet-50 was used as the backbone network to reduce the gradient dispersion phenomenon and improve the accuracy of the model. The anchor point parameters of the RPN network were improved and optimized for the characteristics of small weld defects. At the same time, the FPN network was introduced to extract the defect features. Finally, a comparative experiment with other detection methods was carried out. The experimental results showed that the m_AP value of the data set on the model reached 96.33%, which can meet the requirements of automatic auxiliary detection of X-ray weld defects.