Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 230200089-6.doi: 10.11896/jsjkx.230200089

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Hue Augmentation Method for Industrial Product Surface Defect Images

LUO Yuetong1,2, LI Chao1, DUAN Chang1, ZHOU Bo1,2   

  1. 1 School of Computer and Information,Hefei University of Technology,Hefei 230601,China
    2 Engineering Research Center of Safety Critical Industrial Measurement and Control Technology,Ministry of Education,Hefei 230009,China
  • Published:2023-11-09
  • About author:LUO Yuetong,born in 1978,Ph.D,professor,is a member of China Computer Federation.His main research interests include image processing and scientific visualization.
    ZHOU Bo,born in 1981,Ph.D,associate professor.His main research interests include deep learning,image proces-sing,and artificial intelligence.
  • Supported by:
    National Natural Science Foundation of China(61602146) and National Basic Research Program of China(2017YFB1402200).

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Abstract: The hue distribution of industrial sampling data and the spatial distribution of defects are often different from test data,which often leads to poor performance of defect detection models based on deep learning.Therefore,data augmentation based on generative adversarial networks(GAN) is a common solution.Two GANs (HC-GAN and T-GAN) are designed to perform hue augmentation and defect location augmentation respectively.By constructing content consistency module and hue controlled module,HC-GAN can achieve hue augmentation based on reference data without changing defect characteristics.By pairing the input and output data,T-GAN realizes the defect location transfer.In addition,two GANs can also be used in tandem to achieve both hue augmentation and position transfer.Finally,hue distribution statistics and object detection effect tests are carried out on the generated data.The results show that the data generated by the proposed method can achieve hue augmentation and position augmentation,and improve the accuracy of surface defect detection of industrial products.

Key words: GAN, Deep learning, Data augmentation, Defect detection

CLC Number: 

  • TP391.41
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