Laser & Optoelectronics Progress, Volume. 58, Issue 4, 0410016(2021)

Strip Defect Classification Based on Improved Generative Adversarial Networks and MobileNetV3

Jiang Chang1, Shengqi Guan1,2、*, Hongyu Shi3, Luping Hu1, and Yiqi Ni1
Author Affiliations
  • 1School of Mechanical and Electronic Engineering, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
  • 2Shaoxing Keqiao West-Tex Textile Industry Innovative Institute, Shaoxing, Zhejiang 312030, China
  • 3School of Computer Science, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
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    Figures & Tables(6)
    Structure of the generator network
    Structure of the discriminator network
    Part of the original strip steel defect images
    Images obtained by different network training. (a) 100 epochs; (b) 300 epochs; (c) 500 epochs
    • Table 1. Structure of the improved MobileNetV3

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      Table 1. Structure of the improved MobileNetV3

      OperatorResolutionChannelLayer
      Conv 3×364×64161
      Bottleneck 3×332×32242
      Bottleneck 5×516×16402
      Bottleneck 5×58×8802
      Conv 1×1 & Pooling4×41201
      Conv 1×11×12401
      Conv 1×11×161
    • Table 2. Image classification performance of different networks

      View table

      Table 2. Image classification performance of different networks

      NetworkParameter amount /millionPredictedtime /msAccuracy /%Precision /%Recall /%F1
      Original MobileNetV31.2427292.0592.5491.470.92
      Improved MobileNetV30.0923194.6795.2194.790.95
      VGG1617.3532093.8594.3393.670.94
      ResNet3421.2831891.2391.4190.590.91
      DenseNet1216.9531594.3694.5094.100.94
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    Jiang Chang, Shengqi Guan, Hongyu Shi, Luping Hu, Yiqi Ni. Strip Defect Classification Based on Improved Generative Adversarial Networks and MobileNetV3[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410016

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    Paper Information

    Category: Image Processing

    Received: Jul. 6, 2020

    Accepted: Aug. 6, 2020

    Published Online: Feb. 24, 2021

    The Author Email: Guan Shengqi (sina1300841@163.com)

    DOI:10.3788/LOP202158.0410016

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