• Journal of Applied Optics
  • Vol. 44, Issue 3, 677 (2023)
Jinyao HOU1, Weiguo LIU1,*, Shun ZHOU1, Aihua GAO1..., Shaobo GE1 and Xiangguo XIAO2|Show fewer author(s)
Author Affiliations
  • 1College of Photoelectric Engineering, Xi'an Technological University, Xi'an 710021, China
  • 2Xi'an Institute of Applied Optics, Xi'an 710065, China
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    DOI: 10.5768/JAO202344.0305003 Cite this Article
    Jinyao HOU, Weiguo LIU, Shun ZHOU, Aihua GAO, Shaobo GE, Xiangguo XIAO. Image classification of optical element surface defects based on convolutional neural network[J]. Journal of Applied Optics, 2023, 44(3): 677 Copy Citation Text show less
    References

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    [7] WANG Kungjeng, HAO Fanjiang, LEE Yaxuan, A multiple-stage defect detection model by convolutional neural network[J]. Computers & Industrial Engineering, 2022, 168: 108096.

    [15] Makantasis K, Karantzalos K, Doulamis A, et al. Deep supervised learning for hyperspectral data classification through convolutional neural networks[C]// Geoscience & Remote Sensing Symposium.USA: IEEE, 2015: 4959-4962.

    [16] BANDHU A, ROY S S. Classifying multi-category imagesusing deep learning: a convolutional neural network model[C]//IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology. Ban- galore: IEEE,2017: 915-919.

    [18] DING X, GUO Y, DING G, et al. ACNet: strengthening the kernel skeletons for powerful CNN via asymmetric convolution blocks[C]// International Conference on Computer Vision, USA: IEEE, 2019: 1911-1920.

    Jinyao HOU, Weiguo LIU, Shun ZHOU, Aihua GAO, Shaobo GE, Xiangguo XIAO. Image classification of optical element surface defects based on convolutional neural network[J]. Journal of Applied Optics, 2023, 44(3): 677
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