Simulation Dataset Preparation and Hybrid Training for Deep Learning in Defect Detection Using Digital Shearography

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 2π 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.

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“…A simulation dataset and hybrid training, based on digital shearography images, in deep learning, were performed for defect detection in various kinds of materials (epoxy carbon, E glass fibre, ABS thermoplastic, etc.) [ 38 ]. The results showed that a simulation dataset, generated without any real defective specimen, shearography system or manual experiment, can greatly improve the generalisation of a deep learning network when the number of experimental training images is small.…”