• 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
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    Based on an unsupervised network, a method for cell phone screen defect segmentation is proposed to solve the problem of low accuracy in cell phone screen defect detection. First, an image reconstruction network with multiscale features is constructed through an unsupervised convolutional denoising autoencoder, which reconstructs the multilayer background texture image from the defect image. Then, the defect and multilayer-reconstructed images are subtracted separately to eliminate the influence of the background texture. Finally, adaptive threshold strategy is used for segmentation and the segmentation results are fused to improve the accuracy of defect segmentation. To improve the reconstruction performance, an improved loss function is proposed to train the reconstruction network. Based on an image pixel histogram, the triangle method is used for global adaptive threshold segmentation to improve the segmentation accuracy. The experimental result shows that the proposed method can predict the cell phone screen defect area, reaching 90.30% accuracy. The accuracy and real time of the proposed method meet industrial requirements and it is practical.
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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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    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)