Automatic defect recognition in ultrasonic infrared thermography by deep learning of temporal signals

Jinfang Xie; Zhi Zeng; Yibin Tian

doi:10.1117/12.3009200

29 December 2023 Automatic defect recognition in ultrasonic infrared thermography by deep learning of temporal signals

Jinfang Xie, Zhi Zeng, Yibin Tian

Proceedings Volume 12976, Eighth Asia Pacific Conference on Optics Manufacture and Third International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2023); 129761F (2023) https://doi.org/10.1117/12.3009200
Event: 8th Asia Pacific Conference on Optics Manufacture & 3rd International Forum of Young Scientists on Advanced Optical Manufacturing, 2023, Shenzhen, China

Abstract

In ultrasonic infrared thermography, ultrasonic waves are used to generate heat in a target being tested. The heat causes temperature variations in the target, which can be captured by an infrared camera. By analyzing the thermal patterns, it is possible to identify defects in the target. We propose a two-stage automatic defect recognition method using temporal signals from ultrasonic infrared thermography. First, the temperature rise thermogram is segmented using Otsu’s method to remove most of the noise signals. Second, the heat variation signals are extracted based on the temperature rise thermogram sequences. Then, a deep learning model, termed temporal signal defect identification network (TSDI-Net), is designed to accomplish automatic recognition of defect signals. The TSDI-Net consists of a time-space feature module, a hybrid two stream feature module, a global average pooling module, fully connected layers and a Softmax output layer. To verify the effectiveness of the proposed TSDI-Net, five models in the literature, ACN-LSTM, MACNN, OS-CNN, ResCNN, and XceptionTime, were selected for comparison. Ultrasonic infrared thermography image sequences from 153 components are divided into a training set, a validation set, and a test set with ratios of 75%, 10%, and 15%. Results show that the proposed method outperforms the existing defect recognition deep learning models.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jinfang Xie, Zhi Zeng, and Yibin Tian "Automatic defect recognition in ultrasonic infrared thermography by deep learning of temporal signals", Proc. SPIE 12976, Eighth Asia Pacific Conference on Optics Manufacture and Third International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2023), 129761F (29 December 2023); https://doi.org/10.1117/12.3009200

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
5 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Thermography

Ultrasonics

Image segmentation

Deep learning

Feature extraction

Thermal modeling

Education and training

Show All Keywords

Keywords/Phrases

Search In:

Publication Years