• Laser & Optoelectronics Progress
  • Vol. 58, Issue 20, 2015001 (2021)
Qi Meng1, Hua Miao1、*, Lin Li1, Bo Guo1, Tingting Liu1, and Shilong Mi2
Author Affiliations
  • 1School of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun, Jilin 130022, China
  • 2R&D Center, Dongguan Yutong Optical Technology Co.,Ltd, Dongguan, Guangdong 523841, China;
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    DOI: 10.3788/LOP202158.2015001 Cite this Article Set citation alerts
    Qi Meng, Hua Miao, Lin Li, Bo Guo, Tingting Liu, Shilong Mi. Data Enhancement of Lens Defect Based on Dual Channel Generative Adversarial Networks[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2015001 Copy Citation Text show less
    References

    [1] Sun L, Liu C, Yao H B. Automatic detection of water-mask for resin glasses by machine vision[J]. Journal of Jiangsu University (Natural Science Edition), 39, 425-430(2018).

    [2] Tao X, Hou W, Xu D. A survey of surface defect detection methods based on deep learning[J]. Acta Automatica Sinica, 47, 1017-1034(2021).

    [3] Li S B, Yang J, Wang Z et al. Review of development and application of defect detection technology[J]. Acta Automatica Sinica, 46, 2319-2336(2020).

    [4] Li D J, Li R H. Mug defect detection method based on improved Faster RCNN[J]. Laser & Optoelectronics Progress, 57, 041515(2020).

    [5] Zhang G S, Ge G Y, Zhu R H et al. Gear defect detection based on the improved YOLOv3 network[J]. Laser & Optoelectronics Progress, 57, 121009(2020).

    [6] Li S Y, Fu G Y, Cui Z M et al. Data augmentation in SAR images based on multi-scale generative adversarial networks[J]. Laser & Optoelectronics Progress, 57, 201018(2020).

    [7] Chang J, Guan S Q, Shi H Y et al. Strip defect classification based on the improved generative adversarial networks and MobileNetV3[J]. Laser & Optoelectronics Progress, 58, 0410016(2021).

    [8] Wang S, Guo R F, Dong Z Y et al. Data enhancement method for deep learning in mixed reality assembly inspection[J]. Computer Integrated Manufacturing Systems, 27, 716-727(2021).

    [9] Meng L, Zhong J P, Li N. Generating algorithm of medical image simulation data sets based on GAN[J]. Journal of Northeastern University (Natural Science), 41, 332-336(2020).

    [10] Goodfellow I, Pougetabadie-Abadie J, Mirza M et al. Generative adversarial networks[J]. Advances in Neural Information Processing Systems, 3, 2672-2680(2014).

    [11] Mirza M, Osindero S. Conditional generative adversarial nets[EB/OL]. (2014-11-06)[2020-04-19]. https: //arxiv.org/abs/1411.1784

    [12] Tan B D, Yang J, Lai Q P et al. Data augment method for power system transient stability assessment based on improved conditional generative adversarial network[J]. Automation of Electric Power Systems, 43, 149-157(2019).

    [13] Isola P, Zhu J Y, Zhou T H et al. Image-to-image translation with conditional adversarial networks[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 5967-5976(2017).

    [14] Zhao H X, Shi H B, Wu J et al. Research on imbalanced learning based on conditional generative adversarial networks[J]. Control and Decision, 619-628(2021).

    [15] Dar S U H, Yurt M, Karacan L et al. Image synthesis in multi-contrast MRI with conditional generative adversarial networks[J]. IEEE Transactions on Medical Imaging, 38, 2375-2388(2019).

    [16] Arjovsky M, Bottou L. Towards principled methods for training generative adversarial networks[EB/OL]. (2017-01-17)[2020-04-19]. https: //arxiv.org/abs/1701.04862

    [17] Arjovsky M, Chintala S, Bottou L. Wasserstein GAN[EB/OL]. (2017-01-26)[2020-04-19]. https://arxiv.org/abs/1701.07875

    [18] Cui Z Y, Zhang M R, Cao Z J et al. Image data augmentation for SAR sensor via generative adversarial nets[J]. IEEE Access, 7, 42255-42268(2019).

    [19] Mu D, Meng W, Zhao S H et al. Intelligent optical communication based on Wasserstein generating adversarial network[J]. Chinese Journal of Lasers, 47, 1106005(2020).

    [20] Zhu J Y, Park T, Isola P et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[EB/OL]. (2018-02-19)[2018-03-01]. https://arxiv.org/abs/1703.10593

    [21] Li J X, Sun L J, Wang W J. Appearance design method of packaging product based on dual discriminator GAN[J]. Packaging Journal, 12, 77-83(2020).

    [22] Chang Y S, Lafata K, Segars W P et al. Development of realistic multi-contrast textured XCAT (MT-XCAT) phantoms using a dual-discriminator conditional-generative adversarial network (D-CGAN)[J]. Physics in Medicine and Biology, 65, 065009(2020).

    [23] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA, 779-788(2016).

    [24] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA, 6517-6525(2017).

    [25] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. (2018-04-08) [2020-05-20]. https: //arxiv.org/abs/1804.02767

    Qi Meng, Hua Miao, Lin Li, Bo Guo, Tingting Liu, Shilong Mi. Data Enhancement of Lens Defect Based on Dual Channel Generative Adversarial Networks[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2015001
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