PGA-Net: Pyramid Feature Fusion and Global Context Attention Network for Automated Surface Defect Detection

Surface defect detection is a critical task in industrial production process. Nowadays, there are lots of detection methods based on computer vision and have been successfully applied in industry, they also achieved good results. However, achieving full automation of surface defect detection remains a challenge, due to the complexity of surface defect, in intraclass. While the defects between interclass contain similar parts, there are large differences in appearance of the defects. To address these issues, this article proposes a pyramid feature fusion and global context attention network for pixel-wise detection of surface defect, called PGA-Net. In the framework, the multiscale features are extracted at…

• NN
  National Natural Science Foundation of China

  Awards: 51374063, 51805078
• FR
  Fundamental Research Funds for the Central Universities

  Award: N170304014
• NK
  National Key Research and Development Program of China Stem Cell and Translational Research

  Award: 2017YFB0304200