Automated detection of weld defects based on the improved Cascade Mask R-CNN

In weld defect detection, due to differences in sample distribution, the single threshold-based object detection algorithms may lead to low detection accuracy when locating and identifying defects in x-ray images. To address this problem, we propose a weld defect detection method based on the cascaded structure model. More specifically, we improve Cascade Mask R-CNN by using deformable convolution, feature pyramid network, an efficient global context modeling, and self-setting the aspect ratios of anchors. In addition, we introduce the data augmentations of flipping and crop-paste to enhance the size of the dataset. Experiments show that the improved Cascade Mask R-CNN significantly realizes better detection accuracy than other classic two-stage object detection models, especially for minor defects such as round defects and cracks, and verify that the improved Cascade Mask R-CNN partially counteracts the effects of differences in the defect samples’ distribution.