Volume 58 Issue 24
Dec 2022
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JOURNAL OF MECHANICAL ENGINEERING  > 2022  >  58(24): 32-40.
LI Ke, QI Yang, SU Lei, GU Jiefei, SU Wensheng. Visual Inspection of Steel Surface Defects Based on Improved Auxiliary Classification Generation Adversarial Network[J]. JOURNAL OF MECHANICAL ENGINEERING, 2022, 58(24): 32-40. doi: 10.3901/JME.2022.24.032
Citation: LI Ke, QI Yang, SU Lei, GU Jiefei, SU Wensheng. Visual Inspection of Steel Surface Defects Based on Improved Auxiliary Classification Generation Adversarial Network[J]. JOURNAL OF MECHANICAL ENGINEERING, 2022, 58(24): 32-40. doi: 10.3901/JME.2022.24.032
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Visual Inspection of Steel Surface Defects Based on Improved Auxiliary Classification Generation Adversarial Network

doi: 10.3901/JME.2022.24.032
  • 1.

    School of Mechanical Engineering, Jiangnan University, Wuxi 214122

  • 2.

    Special Equipment Safety Supervision Inspection Institute of Jiangsu Province, Nanjing 210003

  • Received Date: 20 Jan 2022
  • Rev Recd Date: 03 Sep 2022
    • Available Online: 07 Mar 2024
  • Issue Publish Date: 20 Dec 2022
    Abstract
  • In order to improve the accuracy of steel surface defect detection in small sample environment, a new method of steel surface defect detection based on the improved auxiliary classifier generative adversarial network (ACGAN) is proposed. Firstly, the residual block is used to optimize the network of ACGAN to improve the feature extraction ability of the model; Secondly, in order to improve the stability of model training, spectral norm normalization is added to the convolution layer of the network to prevent abnormal gradient changes of the model. Then, the loss function of discriminator is optimized based on positive-unlabeled classification to improve the quality of generated samples. At the same time, a gradient penalty is added to the loss function to constrain the gradient of the discriminator in order to alleviate the mode collapse of the Generative Adversarial Network. Finally, the sample expansion is realized through the adversarial optimization training of generator and discriminator. We conducted experiments on steel surface defect datasets to validate the proposed method can accurately and effectively detect steel surface defects in a small sample environment. Compared with the classical support vector machine, ResNet50 and some small sample classification models, the proposed method has higher detection accuracy.

     

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