Mr. Xu Bao Profile

Xu Bao

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 11 October 2023 Paper

Domain adaptation strategy for aging and damage classification model of high chromium martensitic heat-resistant steel based on transfer learning

Yaling Liu, Xu Yang, Hongbin Duan, Xu Bao

Proceedings Volume 12918, 1291828 (2023) https://doi.org/10.1117/12.3009285

KEYWORDS: Data modeling, Education and training, Machine learning, Feature extraction, Performance modeling, Deep learning, Convolution, Neural networks, Image classification, Visual process modeling

Read Abstract +

Using deep learning to study the aging and damage classification of high-chromium martensite microscopic metallography requires a large amount of labeled data; usually a pre-trained deep learning model is used to deal with this problem, however, the pre-trained model contains a large number of available Features, although fine-tuning the entire pre-training model improves the model effect, it consumes a lot of computing power. In view of this, based on migration learning, this paper divides the VGG network into several parts according to the order of network reasoning from the perspective of domain adaptation, and divides and identifies the functions of each part from the perspective of down sampling; using high-chromium martensitic metallographic aging Each part of the network is fine-tuned separately with the impairment dataset. The research results show that the fine-tuning part of the model converges faster and saves more computing power than the fine-tuning of the entire model; different functional parts of the model have different effects on the final effect of the model, and fine-tuning the module closest to the input has less impact on the final effect of the model. In this way, a more efficient domain adaptation strategy for the aging and damage classification model of high-chromium martensitic heat-resistant steel is obtained.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years