Mr. Qiang Fang Profile

Qiang Fang

at Univ Laval

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 | 23 April 2020 Presentation + Paper

Automatic defect detection in infrared thermography by deep learning algorithm

Qiang Fang, Ba Diep Nguyen, Clemente Ibarra Castanedo, Yuxia Duan, Xavier Maldague

Proceedings Volume 11409, 114090T (2020) https://doi.org/10.1117/12.2555553

KEYWORDS: Infrared radiation, Fiber reinforced polymers

Read Abstract +

Non-destructive Evaluation (NDE) is a field that is used to identify all kinds of structural damage in an object of interest without resulting in any permanent damage or modification to the object. This field has been intensively investigated for many years. Among several research topics in this field, the supervised defect detection methods are among the most innovative and challenging. In recent years, the deep learning field of artificial intelligence has made remarkable progress in image processing applications. Deep learning has shown its ability to overcome most of the disadvantages suffered by previous existing approaches in a great number of applications. In this paper, we propose a deep learning architecture based on infrared thermography inspection intended to automatically identify defects (including internal and invisible cracks, delamination, etc.) efficiently and accurately. We studied the proposed deep learning algorithms to achieve automatic defect detection and precise localization (subsurface defects case) from different thermal image sequences. To evaluate the efficiency and robustness of the proposed methodology, specimens containing artificial defects were selected for experimental configuration.

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