Prof. Jianchun Fan Profile

Prof. Jianchun Fan

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 | 27 May 2024 Paper

Promptable model for premium connection sealing surface detection and segmentation

Shuai Yang, Jianchun Fan, Chunmeng Tian, Ting Han, Kaizhe Wei

Proceedings Volume 13169, 1316904 (2024) https://doi.org/10.1117/12.3029944

KEYWORDS: Image segmentation, Ultrasonics, Object detection, Image processing

Read Abstract +

When applying the tubing ultrasonic testing technology to evaluate the contact condition of the metal-to-metal sealing surface of premium connection, it is necessary to judge the ultrasonic reflection amplitude image of the manual contact interface to observe whether there is sealing defect. At present, the images of phased array ultrasonic testing results usually require professionals to rely on technical knowledge to judge, the analysis has low efficiency and strong evaluation subjectivity. Therefore, there is an urgent need for an intelligent method to identify the location, range and type of sealing defects in ultrasonic images accurately and efficiently, so as assisting or replacing manual operations. Aiming at the problems of heavy reliance on data quality and inflexible segmentation effect during the process of identifying the sealing surface region using the original Mask R-CNN network, the approach described in this paper enhances the model by incorporating the Segment Anything Model(SAM) and employs prompts to guide the object detection model in generating masks that fulfill various criteria. Experiments show that the method adopted in this paper can not only correctly identify the sealing surface location, but also, compared with the original Mask R-CNN network model, it can output a segmentation mask that meets the demand according to the prompts of different segmentation criteria, and the obtained sealing surface region is closer to the theoretical segmentation region.

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