Weld Defect Segmentation in X-ray Image with Boundary Label Smoothing

Abstract

Weld defect segmentation (WDS) is widely used to detect defects from X-ray images for welds, which is of practical importance for manufacturing in all industries. The key challenge of WDS is that the labeled ground truth of defects is usually not accurate because of the similarities between the candidate defect and noisy background, making it difficult to distinguish some critical defects, such as cracks, from the weld line during the inference stage. In this paper, we propose boundary label smoothing (BLS), which uses Gaussian Blur to soften the labels near object boundaries to provide an appropriate representation of inaccuracy and uncertainty in ground truth labels. We incorporate BLS into dice loss, in combination with focal loss and weighted cross-entropy loss as a hybrid loss, to achieve improved performance on different types of segmentation datasets.