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Automatic Detection of Welding Defects using Deep Neural Network

Journal of physics. Conference series, 2018-01, Vol.933 (1), p.12006 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2018. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/933/1/012006

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  • Title:
    Automatic Detection of Welding Defects using Deep Neural Network
  • Author: Hou, Wenhui ; Wei, Ye ; Guo, Jie ; Jin, Yi ; Zhu, Chang'an
  • Subjects: Artificial neural networks ; Automatic welding ; Image classification ; Neural networks ; Weld defects ; Welded joints
  • Is Part Of: Journal of physics. Conference series, 2018-01, Vol.933 (1), p.12006
  • Description: In this paper, we propose an automatic detection schema including three stages for weld defects in x-ray images. Firstly, the preprocessing procedure for the image is implemented to locate the weld region; Then a classification model which is trained and tested by the patches cropped from x-ray images is constructed based on deep neural network. And this model can learn the intrinsic feature of images without extra calculation; Finally, the sliding-window approach is utilized to detect the whole images based on the trained model. In order to evaluate the performance of the model, we carry out several experiments. The results demonstrate that the classification model we proposed is effective in the detection of welded joints quality.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/933/1/012006
  • Source: IOPscience (Open Access)
    Institute of Physics Open Access Journal Titles
    GFMER Free Medical Journals
    ProQuest Central
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