Welding defects classification by weakly supervised semantic segmentation

Abstract

Radiographic non-destructive evaluation (NDE) is an essential technique for understanding defects in welds. These radiographs require certified workers to interpret them to identify the presence of defects. Recent deep learning techniques, primarily semantic segmentation, could help welding defect detection and classification. Using image segmentation technology to obtain performance evaluations of the presence, location, and size of defects can improve the stability of defect evaluations while saving a great deal of time. However, supervised instance segmentation requires many manually implemented pixel-level annotations, dramatically increasing the difficulty and cost of achieving non-destructive evaluations. In our work, the weakly supervised semantic segmentation based on the Cut-Cascade RCNN model is used to classify defects. The cascade RCNN obtains the region of interest (ROI) and classification information. In the ROI, adaptive threshold segmentation of the defects is implemented, and the image is filtered to obtain the mask information. The accuracy of using the Cut-Cascade RCNN model in our x-ray dataset size can reach 90.15%.