Optics and Precision Engineering, Volume. 31, Issue 16, 2406(2023)

Equipment fault dataset amplification method combine 3D model with improved CycleGAN

Baoping LI, Hengyi QI*, Manli WANG, and Po WEI
Author Affiliations
  • College of Physics and Electronic Information, Henan Polytechnic University, Jiaozuo454000, China
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    References(27)

    [1] SHIN H et al. Enhancement of multi-class structural defect recognition using generative adversarial network[J]. Sustainability, 13, 12682(2021).

    [2] SHORTEN C, KHOSHGOFTAAR T M. A survey on image data augmentation for deep learning[J]. Journal of Big Data, 6, 1-48(2019).

    [3] CHEN L, WANG H J, MENG X H. Remote sensing image dataset expansion based on generative adversarial networks with modified shuffle attention[J]. Sensors, 21, 4867(2021).

    [4] ZHANG H G, HU X G, MA D Z et al. Insufficient data generative model for pipeline network leak detection using generative adversarial networks[J]. IEEE Transactions on Cybernetics, 52, 7107-7120(2022).

    [5] SANDFORT V, YAN K, PICKHARDT P J et al. Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks[J]. Scientific Reports, 9, 16884(2019).

    [6] SU W C, YE H, CHEN S Y et al. DrawingInStyles: portrait image generation and editing with spatially conditioned StyleGAN[J]. IEEE Transactions on Visualization and Computer Graphics(2022).

    [7] GOODFELLOW I, POUGET-ABADIE J, MIRZA M et al. Generative adversarial networks[J]. Communications of the ACM, 63, 139-144(2020).

    [8] RADFORD A, METZ L, CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks[C](2016).

    [9] ARJOVSKY M, CHINTALA S, BOTTOU L. Wasserstein generative adversarial networks[C], 214-223(2017).

    [10] GULRAJANI I, AHMED F, ARJOVSKY M et al. Improved training of wasserstein GANs[C], 5769-5779(9).

    [11] ZHU J Y, PARK T, ISOLA P et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C], 2242-2251(22).

    [12] [12] 12王昊天, 刘庆省, 陈亮, 等. 改进的CycleGAN网络用于水下显微图像颜色校正[J]. 光学 精密工程, 2022, 30(12)1499-1508. doi: 10.37188/OPE.20223012.1499WANGH T, LIUQ S, CHENL, et al. Improved CycleGAN network for underwater microscopic image color correction[J]. Opt. Precision Eng., 2022, 30(12)1499-1508(in Chinese). doi: 10.37188/OPE.20223012.1499

    [13] HU A N, XIE Z, XU Y Y et al. Unsupervised haze removal for high-resolution optical remote-sensing images based on improved generative adversarial networks[J]. Remote Sensing, 12, 4162(2020).

    [14] WANG X T, YU K, WU S X et al. ESRGAN Enhanced Super-Resolution Generative Adversarial Networks[M]. Lecture Notes in Computer Science, 63-79(2019).

    [15] [15] 15郝帅, 吴瑛琦, 马旭, 等. 基于CycleGAN-SIFT的可见光和红外图像匹配[J]. 光学 精密工程, 2022, 30(5)602-614. doi: 10.37188/OPE.20223005.0592HAOS, WUY Q, MAX, et al. Visible and infrared image matching based on CycleGAN-SIFT[J]. Opt. Precision Eng., 2022, 30(5)602-614(in Chinese). doi: 10.37188/OPE.20223005.0592

    [16] [16] 16凡志邈, 夏伟杰, 刘雪. 基于修正CycleGAN的声呐图像库构建方法研究[J]. 声学技术, 2021, 40(6)890-894. doi: 10.3969/j.issn.1000-3630.2021.6.sxjs202106023FANZ M, XIAW J, LIUX. Modified CycleGAN based sonar image library construction[J]. Technical Acoustics, 2021, 40(6)890-894(in Chinese). doi: 10.3969/j.issn.1000-3630.2021.6.sxjs202106023

    [17] [17] 17杨植凯, 卜乐平, 王腾, 等. 基于循环一致性对抗网络的室内火焰图像场景迁移[J]. 光学 精密工程, 2020, 28(3)745-758. doi: 10.3788/ope.20202803.0745YANGZ K, BUL P, WANGT, et al. Scenemigration of indoor flame image based on Cycle-Consistent adversarial networks[J]. Opt. Precision Eng., 2020, 28(3)745-758(in Chinese). doi: 10.3788/ope.20202803.0745

    [18] [18] 18崔克彬, 潘锋. 用于绝缘子故障检测的CycleGAN小样本库扩增方法研究[J]. 计算机工程与科学, 2022, 44(3)509-515. doi: 10.3969/j.issn.1007-130X.2022.03.017CUIK B, PANF. A CycleGAN small sample library amplification method for faulty insulator detection[J]. Computer Engineering and Science, 2022, 44(3)509-515(in Chinese). doi: 10.3969/j.issn.1007-130X.2022.03.017

    [19] [19] 19杜振龙, 沈海洋, 宋国美, 等. 基于改进CycleGAN的图像风格迁移[J]. 光学 精密工程, 2019, 27(8)1836-1844. doi: 10.3788/ope.20192708.1836DUZ L, SHENH Y, SONGG M, et al. Image style transfer based on improved CycleGAN[J]. Opt. Precision Eng., 2019, 27(8)1836-1844(in Chinese). doi: 10.3788/ope.20192708.1836

    [20] DOU H, CHEN C, HU X et al. Asymmetric CycleGAN for image-to-image translations with uneven complexities[J]. Neurocomputing, 415, 114-122(2020).

    [21] PARIS S, HASINOFF S W, KAUTZ J. Local Laplacian filters[J]. ACM Transactions on Graphics, 30, 1-12(2011).

    [22] RONNEBERGER O, FISCHER P, BROX T. U-Net Convolutional Networks for Biomedical Image Segmentation[M]. Lecture Notes in Computer Science, 234-241(2015).

    [23] HE K M, ZHANG X Y, REN S Q et al. Deep residual learning for image recognition[C], 770-778(27).

    [24] ISOLA P, ZHU J Y, ZHOU T H et al. Image-to-image translation with conditional adversarial networks[C], 5967-5976(21).

    [25] BENNY Y, GALANTI T, BENAIM S et al. Evaluation metrics for conditional image generation[J]. International Journal of Computer Vision, 129, 1-20(2021).

    [26] CHEN M J, BOVIK A C. Fast structural similarity index algorithm[J]. Journal of Real-Time Image Processing, 6, 281-287(2011).

    [27] HUYNH-THU Q, GHANBARI M. Scope of validity of PSNR in image/video quality assessment[J]. Electronics Letters, 44, 800-801(2008).

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    Baoping LI, Hengyi QI, Manli WANG, Po WEI. Equipment fault dataset amplification method combine 3D model with improved CycleGAN[J]. Optics and Precision Engineering, 2023, 31(16): 2406

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    Paper Information

    Category: Information Sciences

    Received: Nov. 16, 2022

    Accepted: --

    Published Online: Sep. 5, 2023

    The Author Email: Hengyi QI (mystericq@home.hpu.edu.cn)

    DOI:10.37188/OPE.20233116.2406

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