Enhancing the Efficiency of Defect Image Identification in Computer Decoding of Digital Radiographic Images of Welded Joints in Hazardous Industrial Facilities
IF 0.9 4区 材料科学Q4 MATERIALS SCIENCE, CHARACTERIZATION & TESTING
{"title":"Enhancing the Efficiency of Defect Image Identification in Computer Decoding of Digital Radiographic Images of Welded Joints in Hazardous Industrial Facilities","authors":"S. A. Grigorchenko, V. I. Kapustin","doi":"10.1134/S1061830924602836","DOIUrl":null,"url":null,"abstract":"<p>—This article is devoted to enhancing the efficiency of defect image identification in computer decoding of radiographic images. The work addresses the task of defect image segmentation, as well as models for defect image segmentation on radiographic images in both manual and computer decoding. The distinction between algorithms for detecting and identifying groups, clusters, chains of pores, slag, and metallic inclusions in comparison to manual decoding of images is demonstrated. \nAlgorithms for defect detection and identification for use in digital radiography systems have been developed and experimentally tested on the APC KARS system. The convergence of results between computer and manual decoding reaches 0.85.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":"60 12","pages":"1387 - 1395"},"PeriodicalIF":0.9000,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Russian Journal of Nondestructive Testing","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1134/S1061830924602836","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
引用次数: 0
Abstract
—This article is devoted to enhancing the efficiency of defect image identification in computer decoding of radiographic images. The work addresses the task of defect image segmentation, as well as models for defect image segmentation on radiographic images in both manual and computer decoding. The distinction between algorithms for detecting and identifying groups, clusters, chains of pores, slag, and metallic inclusions in comparison to manual decoding of images is demonstrated.
Algorithms for defect detection and identification for use in digital radiography systems have been developed and experimentally tested on the APC KARS system. The convergence of results between computer and manual decoding reaches 0.85.
期刊介绍：
Russian Journal of Nondestructive Testing, a translation of Defectoskopiya, is a publication of the Russian Academy of Sciences. This publication offers current Russian research on the theory and technology of nondestructive testing of materials and components. It describes laboratory and industrial investigations of devices and instrumentation and provides reviews of new equipment developed for series manufacture. Articles cover all physical methods of nondestructive testing, including magnetic and electrical; ultrasonic; X-ray and Y-ray; capillary; liquid (color luminescence), and radio (for materials of low conductivity).
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
