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Proceedings Paper

Defective samples simulation through neural style transfer for automatic surface defect segment
Author(s): Taoran Wei; Danhua Cao; Xingru Jiang; Caiyun Zheng; Lizhe Liu
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Paper Abstract

Owing to the lack of defect samples in industrial product quality inspection, trained segmentation model tends to overfit when applied online. To address this problem, we propose a defect sample simulation algorithm based on neural style transfer. The simulation algorithm requires only a small number of defect samples for training, and can efficiently generate simulation samples for next-step segmentation task. In our work, we introduce a masked histogram matching module to maintain color consistency of the generated area and the true defect. To preserve the texture consistency with the surrounding pixels, we take the fast style transfer algorithm to blend the generated area into the background. At the same time, we also use the histogram loss to further improve the quality of the generated image. Besides, we propose a novel structure of segment net to make it more suitable for defect segmentation task. We train the segment net with the real defect samples and the generated simulation samples separately on the button datasets. The results show that the F1 score of the model trained with only the generated simulation samples reaches 0.80, which is better than the real sample result.

Paper Details

Date Published: 12 March 2020
PDF: 12 pages
Proc. SPIE 11439, 2019 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems, 1143904 (12 March 2020); doi: 10.1117/12.2540464
Show Author Affiliations
Taoran Wei, Huazhong Univ. of Science and Technology (China)
Danhua Cao, Huazhong Univ. of Science and Technology (China)
Xingru Jiang, Huazhong Univ. of Science and Technology (China)
Caiyun Zheng, Huazhong Univ. of Science and Technology (China)
Lizhe Liu, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 11439:
2019 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems
Jigui Zhu; Kexin Xu; Hai Xiao; Sen Han, Editor(s)

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