DSnet: x-ray weld defect segmentation method based on deep separable convolution structure

Due to the characteristics of multi-scale and complex edge shape of X-ray film weld defects, the segmentation accuracy of conventional segmentation methods is low. Aiming at the characteristics of these defects, this paper proposes an X-ray weld defect segmentation method based on deep separable convolution structure. Firstly, a multi-channel information fusion module is innovatively designed to replace the two consecutive 3×3 convolutions. Secondly, a spatial depth separation attention mechanism is added after the skip connection. Finally, a tail feature information cascade module is proposed to fuse different levels of feature information. This method can show high performance when segmenting X-ray film weld defects with multi-scale and complex edge shapes. The experimental results on the public dataset GDX-ray show that the proposed method is 6.1 % and 6.4 % higher than the classical Unet method in DSC value and precision P respectively, which effectively improves the segmentation accuracy of X-ray weld defects.