Review on Computer Aided Weld Defect Detection from Radiography Images

• Published in: Applied sciences Vol. 10; no. 5; p. 1878
• Main Authors: Hou, Wenhui, Zhang, Dashan, Wei, Ye, Guo, Jie, Zhang, Xiaolong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2020
• Subjects: Automation; Classification; classifier; Deep learning; defect detection; Digitization; feature extraction; Fuzzy sets; image processing; Methods; Noise; Quality; radiographic image; Radiography; Researchers
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10051878

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.