Review on Computer Aided Weld Defect Detection from Radiography Images
Source abstract
Abstract
The weld defects inspection from radiography films is critical for assuring the serviceability and safety of weld joints. The various limitations of human interpretation made the development of innovative computer-aided techniques for automatic detection from radiography images an interest point of recent studies. The studies of automatic defect inspection are synthetically concluded from three aspects: pre-processing, defect segmentation and defect classification. The achievement and limitations of traditional defect classification method based on the feature extraction, selection and classifier are summarized. Then the applications of novel models based on learning(especially deep learning) were introduced. Finally, the achievement of automation methods were discussed and the challenges of current technology are presented for future research for both weld quality management and computer science researchers.
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Research context
OpenAlex topics: Welding Techniques and Residual Stresses · Non-Destructive Testing Techniques · Hydrogen embrittlement and corrosion behaviors in metals
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Engineering, Anhui Agriculture University, No. 130 West Changjiang Road, Hefei 230026, China
Intelligent Agricultural Machinery Laboratory of Anhui Province, Hefei 230026, China
Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230027, China
Funding
National Natural Science Foundation of China
References (67)
Show source-provided references
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