Semiconductor Optoelectronics, Volume. 43, Issue 5, 955(2022)

Research on Crack Sample Expansion and Recognition Based on MDCGAN

XIE Yonghua1 and QI Yang2
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    References(11)

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    [4] [4] Kim Y, Kwak G H, Lee K D, et al. Performance evaluation of machine learning and deep learning algorithms in crop classification: Impact of hyper-parameters and training sample size[J]. Korean J. of Remote Sensing, 2018, 34(5): 811-827.

    [5] [5] Yu X, Wu X, Luo C, et al. Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework[J]. GI Science & Remote Sensing, 2017, 54(5): 741-758.

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    [7] [7] Huang H W, Li Q T, Zhang D M. Deep learning based image recognition for crack and leakage defects of metro shield tunnel-ScienceDirect[J]. Tunnelling and Underground Space Technology, 2018, 77: 166-176.

    [8] [8] Frid-Adar M, Diamant I, Klang E, et al. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification[J]. Neurocomputing, 2018, 321(10): 321-331.

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    [16] [16] Dewi Christine, Chen Rung-Ching, Liu Yan-Ting, et al. Synthetic data generation using DCGAN for improved traffic sign recognition[J]. Neural Computing and Applications, 2021: 1-16.

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    XIE Yonghua, QI Yang. Research on Crack Sample Expansion and Recognition Based on MDCGAN[J]. Semiconductor Optoelectronics, 2022, 43(5): 955

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    Paper Information

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    Received: Mar. 28, 2022

    Accepted: --

    Published Online: Jan. 27, 2023

    The Author Email:

    DOI:10.16818/j.issn1001-5868.2022032801

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