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 in | Journal of physics. Conference series Vol. 933; no. 1; pp. 12006 - 12015 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Bristol
IOP Publishing
03.01.2018
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| Abstract | 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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| AbstractList | 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. |
| Author | Wei, Ye Zhu, Chang'an Hou, Wenhui Guo, Jie Jin, Yi |
| Author_xml | – sequence: 1 givenname: Wenhui surname: Hou fullname: Hou, Wenhui email: hwh303@mail.ustc.edu.cn organization: School of Engineering Science, University of Science and Technology of China , People"s Republic of China – sequence: 2 givenname: Ye surname: Wei fullname: Wei, Ye organization: School of Engineering Science, University of Science and Technology of China , People"s Republic of China – sequence: 3 givenname: Jie surname: Guo fullname: Guo, Jie organization: School of Engineering Science, University of Science and Technology of China , People"s Republic of China – sequence: 4 givenname: Yi surname: Jin fullname: Jin, Yi email: jinyi08@ustc.edu.cn organization: School of Engineering Science, University of Science and Technology of China , People"s Republic of China – sequence: 5 givenname: Chang'an surname: Zhu fullname: Zhu, Chang'an organization: School of Engineering Science, University of Science and Technology of China , People"s Republic of China |
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| SubjectTerms | Artificial neural networks Automatic welding Image classification Neural networks Physics Weld defects Welded joints |
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| Title | Automatic Detection of Welding Defects using Deep Neural Network |
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