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Open AccessReview

Review on Computer Aided Weld Defect Detection from Radiography Images

by 1,2, 1,2, 3, 3 and 1,2,*
1
School of Engineering, Anhui Agriculture University, No. 130 West Changjiang Road, Hefei 230026, China
2
Intelligent Agricultural Machinery Laboratory of Anhui Province, Hefei 230026, China
3
Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230027, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(5), 1878; https://doi.org/10.3390/app10051878
Received: 31 December 2019 / Revised: 27 February 2020 / Accepted: 2 March 2020 / Published: 10 March 2020
(This article belongs to the Special Issue Intelligent Processing on Image and Optical Information)
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. View Full-Text
Keywords: radiographic image; image processing; feature extraction; classifier; deep learning; defect detection radiographic image; image processing; feature extraction; classifier; deep learning; defect detection
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MDPI and ACS Style

Hou, W.; Zhang, D.; Wei, Y.; Guo, J.; Zhang, X. Review on Computer Aided Weld Defect Detection from Radiography Images. Appl. Sci. 2020, 10, 1878. https://doi.org/10.3390/app10051878

AMA Style

Hou W, Zhang D, Wei Y, Guo J, Zhang X. Review on Computer Aided Weld Defect Detection from Radiography Images. Applied Sciences. 2020; 10(5):1878. https://doi.org/10.3390/app10051878

Chicago/Turabian Style

Hou, Wenhui; Zhang, Dashan; Wei, Ye; Guo, Jie; Zhang, Xiaolong. 2020. "Review on Computer Aided Weld Defect Detection from Radiography Images" Appl. Sci. 10, no. 5: 1878. https://doi.org/10.3390/app10051878

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