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Laser & Optoelectronics Progress, Volume. 58, Issue 20, 2015003(2021)

Cell Phone Screen Defect Segmentation Based on Unsupervised Network

Chaodong Dai1,2, Guoliang Xu2、*, Jiao Mao1,2, Tong Gu1,2, and Jiangtao Luo2
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
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    AbstractGet PDF(in Chinese)
    Figures&Tables (13)
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    References (21)
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    Figures & Tables(13)
    Cell phone screen texture background. (a) Defect-free image; (b) defect image

    Fig. 1. Cell phone screen texture background. (a) Defect-free image; (b) defect image

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    Algorithm overall framework

    Fig. 2. Algorithm overall framework

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    Denoising autoencoder structure

    Fig. 3. Denoising autoencoder structure

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    Segmentation process

    Fig. 4. Segmentation process

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    Residual image pixel distribution

    Fig. 5. Residual image pixel distribution

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    Schematic of triangle method

    Fig. 6. Schematic of triangle method

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    Pseudo-code graph of triangle threshold segmentation

    Fig. 7. Pseudo-code graph of triangle threshold segmentation

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    Background reconstruction and defect segmentation results

    Fig. 8. Background reconstruction and defect segmentation results

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    Comparison of defect segmentation results. (a) Defect images; (b) ground truth; (c) SVD; (d) U-Net; (e) MSCDAE; (f) proposed method

    Fig. 9. Comparison of defect segmentation results. (a) Defect images; (b) ground truth; (c) SVD; (d) U-Net; (e) MSCDAE; (f) proposed method

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    • Table 1. Composition of the dataset

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      Table 1. Composition of the dataset

      CompositionNumber
      Point defect image831
      Line defect image757
      Block defect image968
      Defect-free image3000

    • Table 2. Reconstruction performance of different loss functions

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      Table 2. Reconstruction performance of different loss functions

      Loss functionPSNR /dBSSIMLoss functionPSNR /dBSSIM
      L142.12590.9724SSIM+100L141.87970.9738
      L242.50810.9760SSIM+L242.28280.9795
      SSIM41.77160.978510SSIM+L240.10750.9785
      SSIM+L143.69480.9833100SSIM+L240.99730.9799
      10SSIM+L140.61200.9753SSIM+10L241.10220.9739
      100SSIM+L141.45170.9793SSIM+100L239.79730.9681
      SSIM+10L141.49880.9746

    • Table 3. Reconstruction performance of different network layers

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      Table 3. Reconstruction performance of different network layers

      Number of layersPSNR /dBSSIM
      3-layer37.37690.9641
      4-layer41.94430.9754
      5-layer43.37540.9808
      6-layer41.92290.9742
      7-layer39.16760.9615

    • Table 4. Comparison of experimental index

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      Table 4. Comparison of experimental index

      AlgorithmPrecisonRecallF1-scoremIoU /%
      SVD[1]0.95070.86000.895984.15
      U-Net[8]0.95380.80170.846480.16
      MSCDAE[13]0.95170.92500.933389.10
      Proposed method0.97200.92470.944690.30
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    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

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    Paper Information

    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)

    DOI:10.3788/LOP202158.2015003

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology