Automatic Detection of Welding Defects using Deep Neural Network

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 imag...

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Published inJournal of physics. Conference series Vol. 933; no. 1; pp. 12006 - 12015
Main Authors Hou, Wenhui, Wei, Ye, Guo, Jie, Jin, Yi, Zhu, Chang'an
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 03.01.2018
Subjects
Artificial neural networks
Automatic welding
Image classification
Neural networks
Physics
Weld defects
Welded joints
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Summary: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.
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/933/1/012006