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

By Hongwen Dong, Kechen Song, Yu He, Jing Xu, Yunhui Yan, Q. Meng. IEEE Transactions on Industrial Informatics, 2020

444 citations recorded by the source corpus.

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 first from backbone network. Then the pyramid feature fusion module is used to fuse these features into five resolutions through some efficient dense skip connections. Finally, the global context attention module is applied to the fusion feature maps of adjacent resolution, which allows effective information propagate from low-resolution fusion feature maps to high-resolution fusion ones. In addition, the boundary refinement block is added to the framework to refine the boundary of defect and improve the result of the prediction. The final prediction is the fusion of the five resolutions fusion feature maps. The results of evaluation on four real-world defect datasets demonstrate that the proposed method outperforms the state-of-the-art methods on mean intersection of union and mean pixel accuracy (NEU-Seg: 82.15%, DAGM 2007: 74.78%, MT_defect: 71.31%, Road_defect: 79.54%).

Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/d81421695d5d429a47eefe266986d197d7b313f2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

  1. Ling Shao
  2. Qibin Hou
  3. Ming-Ming Cheng

In Computer Vision and Pattern Recognition

  1. Deng-Ping Fan
  2. Ling Shao
  3. Ming-Ming Cheng
  4. Ge-Peng Ji
  5. Qibin Hou
  6. Yunchao Wei
  7. Jianbing Shen
  8. Luc Van Gool

In Industrial and Manufacturing Engineering

  1. Ming-Ming Cheng

Citations in the corpus