A Method of Printing Defect Detection Based on DCGAN
In recent years, deep learning has been widely used in defect detection. At present, the method can detect large defects, but it is still unable to detect the fine defects accurately. In order to solve this problem, this paper proposes a new method of printing defect detection based on deep convolut...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | Chinese |
| Published: |
Harbin University of Science and Technology Publications
2021-12-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2031 |
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| Summary: | In recent years, deep learning has been widely used in defect detection. At present, the method can detect large defects, but it is still unable to detect the fine defects accurately. In order to solve this problem, this paper proposes a new method of printing defect detection based on deep convolutional general advanced networks (DCGAN) networks. The method improves the detection accuracy by the following points: ①The upper sampling module is added on the basis of the original network, and the loss in the upper sampling is reduced; ②A self-attention mechanism is proposed to generate more complex and accurate images; ③The noise distribution of the image is analyzed statistically, the optimal threshold is determined, the noise is removed and the accurate defect image is obtained. This method adds denoising processing, optimizes the network structure, and improves the accuracy of DCGAN image generation.The experiment shows that the accuracy of the method can be improved by 10% in the defect detection experiment to less than 5 pixels compared with the existing method |
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| ISSN: | 1007-2683 |