• Laser & Optoelectronics Progress
  • Vol. 58, Issue 20, 2015003 (2021)
Dai Chaodong1、2, Xu Guoliang2、*, Mao Jiao1、2, Gu Tong1、2, and Luo Jiangtao2
Author Affiliations
  • 1College of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
  • 2Institute of Electronic Information and Network Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
  • show less
    References
    [1] Liu W, Shao H L, He Y Jet al. Parameter adaptive LCD screen defect detection framework[J]. Journal of Harbin University of Science and Technology, 25, 75-82(2020).
    [2] Lu C J, Tsai D M. Automatic defect inspection for LCDs using singular value decomposition[J]. The International Journal of Advanced Manufacturing Technology, 25, 53-61(2005).
    [3] Chen L M. Detection and classification of Mura defect for TFT-LCD based on computer vision[D](2017).
    [4] Liao M, Liu Y Z, Ou Y J Let al. Automatic detection of Mura defect in TFT-LCD mobile screen based on adaptive local enhancement[J]. Chinese Journal of Liquid Crystals and Displays, 33, 475-482(2018).
    [5] Song W. Smart phone screen defect detection based on deep convolutional neural network[D](2019).
    [6] Yu Z Y, Wu X J, Gu X D. Fully convolutional networks for surface defect inspection in industrial environment[M]. //Liu M, Chen H Y, Vincze M. Computer vision systems. Lecture notes in computer science, 10528, 417-426(2017).
    [7] Chen J W, Liu Z G, Wang H Ret al. Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network[J]. IEEE Transactions on Instrumentation and Measurement, 67, 257-269(2018).
    [8] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. //Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).
    [9] Tabernik D, Šela S, Skvarč Jet al. Segmentation-based deep-learning approach for surface-defect detection[J]. Journal of Intelligent Manufacturing, 31, 759-776(2020).
    [10] Zhang H W, Tang W B, Li P Fet al. Defect detection and location of yarn-dyed shirt piece based on denoising convolutional autoencoder[J]. Basic Sciences Journal of Textile Universities, 32, 119-125,132(2019).
    [11] Zhao Z X, Li B, Dong Ret al. A surface defect detection method based on positive samples[M]. //Geng X, Kang B H. PRICAI 2018: trends in artificial intelligence. Lecture notes in computer science, 11013, 473-481(2018).
    [12] Lv C, Zhang Z T, Shen Fet al. A fast surface defect detection method based on background reconstruction[J]. International Journal of Precision Engineering and Manufacturing, 21, 363-375(2020).
    [13] Yang H, Chen Y F, Song K Yet al. Multiscale feature-clustering-based fully convolutional autoencoder for fast accurate visual inspection of texture surface defects[J]. IEEE Transactions on Automation Science and Engineering, 16, 1450-1467(2019).
    [14] Mei S, Yang H, Yin Z P. An unsupervised-learning-based approach for automated defect inspection on textured surfaces[J]. IEEE Transactions on Instrumentation and Measurement, 67, 1266-1277(2018).
    [15] Liu W Q, Liu Z G, Wang Het al. An automated defect detection approach for catenary rod-insulator textured surfaces using unsupervised learning[J]. IEEE Transactions on Instrumentation and Measurement, 69, 8411-8423(2020).
    [16] Yuan K P, Xi Z H. Image super resolution based on depth jumping cascade[J]. Acta Optica Sinica, 39, 0715003(2019).
    [17] Liu M J, Cao Y Z, Zhu S Yet al. Feature fusion video target tracking method based on convolutional neural network[J]. Laser & Optoelectronics Progress, 57, 041502(2020).
    [18] Jiang Z T, He Y T. Infrared and visible image fusion method based on convolutional auto-encoder and residual block[J]. Acta Optica Sinica, 39, 1015001(2019).
    [19] Bergmann P, Löwe S, Fauser Met al. Improving unsupervised defect segmentation by applying structural similarity to autoencoders[C]. ∥Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, February 25-27, 2019. Prague, Czech Republic, 372-380(2019).
    [20] Zack G W, Rogers W E, Latt S A. Automatic measurement of sister chromatid exchange frequency[J]. The Journal of Histochemistry and Cytochemistry, 25, 741-753(1977).
    [21] Zhao G C, Zhang L, Wu F B. Application of improved median filtering algorithm to image de-noising[J]. Journal of Applied Optics, 32, 678-682(2011).
    Copy Citation Text
    Chaodong Dai, Guoliang Xu, Jiao Mao, Tong Gu, Jiangtao Luo. Cell Phone Screen Defect Segmentation Based on Unsupervised Network[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2015003
    Download Citation
    Category: Machine Vision
    Received: Nov. 25, 2020
    Accepted: Jan. 11, 2021
    Published Online: Oct. 14, 2021
    The Author Email: Xu Guoliang (xugl@cqupt.edu.cn)