A New GAN-Based Approach to Data Augmentation and Image Segmentation for Crack Detection in Thermal Imaging Tests

Lulu Tian,Fuad E Alsaadi,Weibo Liu,Xiaohui Liu,Zidong Wang,Yuhua Cheng

doi:10.1007/s12559-021-09922-w

Abstract

As a popular nondestructive testing (NDT) technique, thermal imaging test demonstrates competitive performance in crack detection, especially for detecting subsurface cracks. In thermal imaging test, the temperature of the crack area is higher than that of the non-crack area during the NDT process. By extracting the features of the thermal image sequences, the temperature curve of each spatial point is employed for crack detection. Nevertheless, the quality of thermal images is influenced by the noises due to the complex thermal environment in NDT. In this paper, a modified generative adversarial network (GAN) is employed to improve the image segmentation performance. To improve the feature extraction ability and alleviate the influence of noises, a penalty term is put forward in the loss function of the conventional GAN. A data preprocessing method is developed where the principle component analysis algorithm is adopted for feature extraction. The data argumentation technique is utilized to guarantee the quantity of the training samples. To validate its effectiveness in thermal imaging NDT, the modified GAN is applied to detect the cracks on the eddy current pulsed thermography NDT dataset.

Highlights

In the past few decades, thermography nondestructive testing (NDT) methods have been widely applied to device assessment and component inspection in additive manufacturing, aerospace, transportation, electrical engineering and mechanical engineering [1, 2]
It is known that the thermography NDT methods have the advantages of fast detection speed, high detection accuracy and nondestructive evaluation [3,4,5]
It should be pointed out that the infrared thermography NDT methods can be divided into two types

Summary

Introduction

In the past few decades, thermography nondestructive testing (NDT) methods have been widely applied to device assessment and component inspection in additive manufacturing, aerospace, transportation, electrical engineering and mechanical engineering [1, 2]. Similar to other thermography methods, it is a challenging task in ECPT to build a physical model to accurately describe the NDT process because of complex thermal environment and various disturbances In this context, a reasonable yet effective way is to deal with the acquired ECPT images for crack detection by using the data analysis techniques. A reasonable yet effective way is to deal with the acquired ECPT images for crack detection by using the data analysis techniques Thanks to their powerful capabilities in feature extraction, machine learning algorithms have been widely utilized in thermal image analysis [4, 5, 9,10,11,12,13]. The conclusions and the future work are summarized in "Conclusion"

Background

Methodology

40 Begining for heating

Results and Discussion