###### Abstract  
Since real experimental shearography images are usually few, the application of deep learning for defect detection in digital shearography is limited. A simulation dataset preparation method of shearography images is proposed in this paper. Firstly, deformation distributions are estimated by finite element analysis (FEA); secondly, phase maps are calculated according to the optical shearography system; finally, simulated shearography images are obtained after 2\(\pi\) modulus and gray transform. Various settings in the parameters of object, defect, load and shearing in those three steps could prepare a diverse simulation dataset for deep learning. Together with the real experimental images taken from a shearography setup, hybrid trainings of deep learning for defect detection are performed and discussed. The results show that a simulation dataset, generated without any real defective specimen, shearography system or manual experiment, can greatly improve the generalization of a deep learning network when the number of experimental training images is small.  
defect detection; shearography image; deep learning; simulation dataset hybrid training +
Footnote †: journal:  
[PERSON] et al. proposed a binary defect classification method using CNN for testing the integrity of a glass fiber patch and a metallic oil pipeline. About 300 shearography image samples grouped into two types of defects (major defect and minor/no defect) were trained and tested with a best accuracy of 79%. [PERSON] et al. applied a Faster R-CNN model for bonding defect identification on a cylindrical surface. A dataset of 200 shearography images was trained and tested with a high recognition rate of large defects, but a low recognition rate of small defects. [PERSON] et al. also used an ensemble hybrid Faster R-CNN model for tire quality assessment, where 359 tread shearography images were trained and tested with a best accuracy of 89.16%. In the above three references, the training datasets are 200 or 300 images in size, which is low for deep learning. In general, object detection accuracy is raised when the training dataset is enlarged. However, unlike datasets of faces, human beings or animals, an ideal and huge dataset of shearography images is inaccessible by open access. [PERSON] et al. designed the U-net CNN given the lack of a large training dataset and analyzed the impact damage in carbon fiber-reinforced plastics (FRP). A dataset with 1905 samples was used, but its effectiveness on a dataset of several hundred images is still ambiguous [12].  
Several hundred shearography images are achievable in-site, but thousands of images are hardly accessible. The preparation of a satisfied shearography dataset for training for deep learning will be time-consuming, costly and perhaps even impossible due to the lack of diverse defective specimens in-site. Therefore, the application of deep learning in defect detection using digital shearography is limited.  
To minimize the effect caused by the lack of experimental shearography datasets, the simulation of shearography images for deep learning is proposed and the effectiveness on the joining of those simulated images for defect detection is discussed in this paper. The generation of simulation shearography images is numerically modeled with multiple parameters according to the production mechanism of a real experimental shearography image. No real defective specimen, shearography system or manual experiment is needed, and diverse simulated shearography images can be prepared with different settings of the model parameters. The proposed dataset preparation method will be labor-saving and efficient.