Weakly supervised aluminum alloy microstructure image semantic  segmentation based on consistency constraint

Jinhao Feng; Xiangchun Zhao

doi:10.1117/12.3031201

19 July 2024 Weakly supervised aluminum alloy microstructure image semantic segmentation based on consistency constraint

Jinhao Feng, Xiangchun Zhao

Author Affiliations +

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131812B (2024) https://doi.org/10.1117/12.3031201
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

To improve the quality of alloys, experts need to intelligently analyze the metallographic images, where semantic segmentation serves as a vital means of achieving this analysis. However, traditional fully supervised semantic segmentation methods often suffer from high annotation costs. Weakly supervised semantic segmentation methods require only simple image-level annotations to achieve high-precision semantic segmentation. This paper constructs a dataset of weakly supervised microstructure images of aluminum alloys, greatly reducing manpower and time costs. Furthermore, addressing the sparsity and incompleteness of Class Activation Maps (CAMs) in traditional weakly supervised methods, a weakly supervised semantic segmentation method for aluminum alloy microstructure images is proposed based on consistency constraints. The method first introduces consistency constraints in the feature extraction process to improve the quality of CAMs. It then uses the improved CAMs as a target cue to generate affinity labels and employs the AffinityNet network and random walk strategy to mine target pixels in non-discriminatory areas, obtaining completer and more detailed pixel-level pseudo labels. Finally, semantic segmentation of the obtained pixel-level pseudo labels is performed using the U-Net network to obtain the final segmentation result. Extensive experimental results demonstrate that our method effectively improves the quality of CAMs, thereby enhancing the performance of the segmentation network.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jinhao Feng and Xiangchun Zhao "Weakly supervised aluminum alloy microstructure image semantic segmentation based on consistency constraint", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131812B (19 July 2024); https://doi.org/10.1117/12.3031201

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image segmentation

Semantics

Content addressable memory

Aluminum

Alloys

Education and training

Matrices

Show All Keywords

Keywords/Phrases

Search In:

Publication Years