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PGA-Net: pyramid feature fusion and global context attention network for automated surface defect detection

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journal contribution
posted on 06.05.2020 by Hongwen Dong, Kechen Song, Yu He, Jing Xu, Yunhui Yan, Qinggang Meng
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 intra-class, while the defects between inter-class contain similar parts, there are large differences in appearance of the defects. To address these issues, this paper proposes a pyramid feature fusion and global context attention network for pixel-wise detection of surface defect, called PGA-Net. In the framework, the multi-scale 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 predict. 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%).

Funding

National Natural Science Foundation of China (51805078, 51374063)

National Key Research and Development Program of China (2017YFB0304200)

Fundamental Research Funds for the Central Universities (N170304014)

History

School

  • Science

Department

  • Computer Science

Published in

IEEE Transactions on Industrial Informatics

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Publication date

2019-12-10

Copyright date

2019

ISSN

1551-3203

eISSN

1941-0050

Language

en

Depositor

Prof Qinggang Meng. Deposit date: 5 May 2020

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