[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).
[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).
[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).
[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).