Review on Computer Aided Weld Defect Detection from Radiography Images Article Swipe

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Wenhui Hou , Dashan Zhang , Ye Wei , Jie Guo , Xiaolong Zhang ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/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.

Related Topics

Computer Science

Welding

Artificial Intelligence

Mechanical Engineering

Medicine

Radiology

Concepts

Computer science Welding Artificial intelligence Automation Segmentation Classifier (UML) Digital radiography Engineering drawing Computer vision Radiography Engineering Mechanical engineering Medicine Radiology

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Type: article
Language: en
Landing Page: https://doi.org/10.3390/app10051878
PDF: https://www.mdpi.com/2076-3417/10/5/1878/pdf?version=1583821442
OA Status: gold
Cited By: 114
References: 63
Related Works: 10
OpenAlex ID: https://openalex.org/W3012268530

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DOI: https://doi.org/10.3390/app10051878

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Title: Review on Computer Aided Weld Defect Detection from Radiography Images

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Language: en

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Publication year: 2020

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Publication date: 2020-03-10

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Authors: Wenhui Hou, Dashan Zhang, Ye Wei, Jie Guo, Xiaolong Zhang

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Landing page: https://doi.org/10.3390/app10051878

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Concepts: Computer science, Welding, Artificial intelligence, Automation, Segmentation, Classifier (UML), Digital radiography, Engineering drawing, Computer vision, Radiography, Engineering, Mechanical engineering, Medicine, Radiology

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Cited by: 114

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