In pipelines, weld regions are the primary areas for defect inspection and a critical guarantee for the validity of defect detection results. However, due to the characteristics of X-ray images of pipeline girth welds, such as their large length and complex background information, existing weld region segmentation techniques are prone to under-segmentation, error segmentation and discontinuous segmentation issues of the weld region. To address these problems, this paper proposes an intelligent segmentation technology for the pipeline girth weld region (PGWR) that fuses data-inherent knowledge. Initially, a deep attention adjust module fused with the U-Net network (DAAM-U-Net) is constructed to perform preliminary weld region segmentation, achieving accurate localisation and overall shape segmentation of the weld regions. Furthermore, constraints based on the data-inherent knowledge from X-ray images of weld regions are established to refine the segmentation, overcoming the challenges of under-segmentation, error segmentation and discontinuous segmentation. The proposed method is illustrated and validated using X-ray images of the PGWR. The results show that the segmentation performance of weld regions in complex backgrounds reaches a mean intersection over union (mIoU) of 94.52% and a Dice coefficient of 86.29%, not only demonstrating the effectiveness of the proposed method but also indicating that the approach of combining artificial intelligence (AI) technology with data-inherent knowledge effectively bridges the gap between AI technologies and their isolated applications in the non-destructive testing field.
Jiang et al. (Sun,) studied this question.