Abstract
As a key industrial equipment, the welding quality of pressure vessels is directly related to the operation safety, and X-ray nondestructive testing is an important means to evaluate the weld quality. In this study, a high-resolution semantic segmentation algorithm based on a double U-shaped network was proposed to meet the requirements of pressure vessel weld defect detection, and accurate detection was realized through innovative three-stage processing flow. Firstly, the weld area was automatically extracted from the original X-ray films (3000+×800 + pixels) by the Gaussian positioning algorithm. Then, a multi-image hybrid stitching technology was proposed to reconstruct the long weld into a standard size of 1500 × 1500, which not only expanded the WSCR data set, but also effectively improved the data imbalance problem. In terms of model architecture, by improving U2Netp and UNet networks, MC-SPP module (multi-connection spatial pyramid pooling), RMAG module (residual multi-add gating recurrent unit), HDC-CBAM module (hybrid dilated convolution-convolutional block attention) and CCM module (cross-layer connection fusion) were integrated to form a cascade network with multi-level feature fusion capability. The experimental results showed that the model could effectively segment the defects such as cracks, pores, slag inclusion, incomplete fusion and incomplete penetration in the weld film, and the mIOU value reached 78.1, which was significantly superior to the existing methods, and provided an efficient and reliable solution for welding defect detection of pressure vessels, which had important engineering application value.