Quasi-real dataset generation and network improvement in defect detection based on shearography

Abstract

Shearography combined with deep learning is increasingly becoming a commonly used approach in non-destructive testing (NDT). However, the validity of deep learning depends on the reliability of the training dataset, and the acquisition of real datasets is costly. In this study, the use of a shearography dataset that contains real noise-generating method is proposed. This dataset can generate random shearing direction and shearing amount defects only after one electronic speckle pattern interference experiment and annotation. Moreover, this study improves the existing semantic segmentation network and identify the defect location for the defect phase map containing noise accurately. Real NDT experiments using composite materials validate the effectiveness of the dataset generation method and semantic segmentation network proposed in this study.