Abstract. This work is concerned with the development and implementation of an image pattern recognition approach to support computational vision systems when it is necessary to automatically check the presence of specific objects on a scene, and, besides, to describe their position, orientation and scale. The developed methodology involves the use of a genetic algorithm to find known 2D object views in the image. The proposed approach is fast and presented a robust performance in several test instances including multiobject scenes, with or without partial occlusion.
This paper presents a pioneering approach for weld bead detection in radiographic images obtained by the Double Wall Double Image (DWDI) technique. Such task constitutes an essential step for several high level processes, such as fully automatic flaw identification on welded joints. Sets of sample pixels, corresponding to candidate solutions provided by a genetic algorithm (GA), are compared to pre-defined synthetic weld bead and pipe models in an image matching procedure. The fitness of each set (individual) is evaluated based on a linear combination of its genotype (evaluated by a heuristic function) and phenotype. The evolutionary process automatically selects the best individual in the population and, thus, provides information such as position, orientation and dimension of the detected object. The proposed approach successfully detects pipes and weld beads in radiographic images of different complexities, encouraging future works.Index Terms-DWDI radiographic images, weld bead detection, genetic algorithms, phenotype and heuristic functions.U.S. Government work not protected by U.S.
The current work contributes to the research in the area of pipelines non-destructive testing by presenting new methodologies for the automatic analysis of welds radiographs. Object recognition techniques based on genetic algorithms were used for the automatic weld bead detection. In addiction, we developed an image digital filter for the detection of defects in the weld bead zone. These methodologies were tested for 120 digital radiographs from carbon steel pipeline welded joints. These images were acquired by a storage phosphor system using double-wall radiographic exposing technique with single-wall radiographic viewing, according to the ASME V code. As a result, even defects that are hard to be detected by human vision are automatically highlighted and extracted from the whole image to be classified in the further stages of the weld inspection process.
This paper introduces new techniques to support industrial radiographic inspection, aiming at automatic corrosion monitoring in pipeline systems. Using the methodologies we proposed, pipeline components to be inspected are automatically detected in the radiographic image, their wall thicknesses are measured and parameters for corrosion detection are computed. For the automatic detection and recognition of pipeline components, we developed a new method that uses image matching techniques in conjunction with genetic algorithms. For measuring the pipe wall thicknesses we used image segmentation techniques based on the analysis of image line profiles. As a result, analysis of pipeline radiographs for corrosion monitoring can be automatically performed, improving the reliability and speed of the inspection process.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.