Slag and Porosity Defective Region Identification in Welding Images Using Computer Vision Techniques

The process of welding is prone to many defects and these defects can cause the formation of many defective regions. It is necessary to identify the regions of defects as these may cause problems and breakages. In this work, we have proposed a method to detect and identify the defects that are commonly seen in seam welds. Manually identifying the detects is not only error prone and time consuming, most of the defects are not visible to the human eyes. In recent days, X-ray images of weld seam are used for this purpose. In this paper we have applied computer vision techniques and proposed an image processing pipeline to generate a binary segmentation of the image to identify the regions of slag and porosity defect seen in weld seams. From the experimental results on the publicly available dataset, GDX-ray images, it could be observed that, there is a significant improvement in detecting various defects with the proposed approach.