CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 13229 > Article
Paper
7 August 2024 Masked self-supervised pre-training for steel corrosion recognition via vision transformer under limited sample conditions
Jingzhuo Zhang, Shaohua Chu, Zhenyu Lai, Guangchao Peng, Hai Lin, Zhuowei Tan, Peng Liu
Author Affiliations +
Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 132290T (2024) https://doi.org/10.1117/12.3038786
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China
Abstract
The identification of steel corrosion is a critical task in the field of engineering maintenance; however, the high cost of collecting corrosion samples has limited the application of traditional identification algorithms. This challenge is particularly acute for Transformer architecture models, which require a substantial amount of training data and thus face significant difficulties in corrosion identification. Addressing this issue, this study explores a pre-training strategy for Transformer architectures aimed at recognizing the state of steel corrosion. By employing masked self-supervised learning for pre-training the model under conditions of limited training samples and subsequently refining it through fine-tuning, this approach enables precise identification of steel corrosion states. Experimental results on a custom datasets demonstrate that this method, without increasing the cost of additional annotations, effectively utilizes the limited sample information for learning and modeling, thereby significantly enhancing model accuracy, which provides new insights and tools for addressing similar engineering problems and suggests that self-supervised learning could become an important technological trend in engineering maintenance, especially in contexts where sample acquisition costs are high.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Jingzhuo Zhang, Shaohua Chu, Zhenyu Lai, Guangchao Peng, Hai Lin, Zhuowei Tan, and Peng Liu "Masked self-supervised pre-training for steel corrosion recognition via vision transformer under limited sample conditions", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 132290T (7 August 2024); https://doi.org/10.1117/12.3038786
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
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
 9 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Corrosion
Data modeling
Education and training
Transformers
Image processing
Visual process modeling
Engineering
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top