A hybrid approach of dynamic image processing and complex network to identify repetitive images of welding defects in radiographs of oil and gas pipelines

Pipelines are the safest as well as the most economical way to transport gas and condensate over long distances. Radiographic images are provided to commentators as a tool to diagnose welding defects in metal lines, so the study of welding in gas and oil pipelines has always been one of the most important areas of non-destructive testing. Expert interpreters are now used in many countries to interpret radiographic films from non-destructive tests. Interpreters can detect the number of pores on the weld surface by viewing radiographic images due to the limited number of these people and their unavailability. In some cases, there are many problems. For human interpretation, radiographic videos must be collected and sent to the interpreter's place of work or residence. The purpose of this article is to provide a method that can be used to interpret radiographic films quickly using conventional image processing methods and identify the welding defects in them and determine whether these defects are duplicates or not. The method of image segmentation is the area growth method. The main feature of this method is its proper performance in images such as radiographic images that have less subject variety. This method separates a part of the image from the rest by determining a pixel in the image as the starting point and expanding the area around this point due to the similarity between the pixels. In this paper, based on the histogram, the start and end image of the welding range is determined automatically. Then a combination of different standard algorithms is applied to identify defects in the image. Then, the key points of the image are extracted, and using them, the corresponding complex dynamic network is drawn and its calculations are performed. The simulation results show that the proposed method covers the shortcomings of the previous methods and in addition to bringing the detection of welding defects by computer closer to human diagnosis and in some cases works better than human performance, it has also made it possible to identify duplicate images.