GANs and alternative methods of synthetic noise generation for domain
  adaption of defect classification of Non-destructive ultrasonic testing
Shaun McKnight S. Gareth Pierce Ehsan Mohseni Christopher MacKinnon Charles MacLeod Tom OHare Charalampos Loukas
This work provides a solution to the challenge of small amounts of training
data in Non-Destructive Ultrasonic Testing for composite components. It was
demonstrated that direct simulation alone is ineffective at producing training
data that was representative of the experimental domain due to poor noise
reconstruction. Therefore, four unique synthetic data generation methods were
proposed which use semi-analytical simulated data as a foundation. Each method
was evaluated on its classification performance of real experimental images
when trained on a Convolutional Neural Network which underwent hyperparameter
optimization using a genetic algorithm. The first method introduced task
specific modifications to CycleGAN, to learn the mapping from physics-based
simulations of defect indications to experimental indications in resulting
ultrasound images. The second method was based on combining real experimental
defect free images with simulated defect responses. The final two methods fully
simulated the noise responses at an image and signal level respectively. The
purely simulated data produced a mean classification F1 score of 0.394.
However, when trained on the new synthetic datasets, a significant improvement
in classification performance on experimental data was realized, with mean
classification F1 scores of 0.843, 0.688, 0.629, and 0.738 for the respective
approaches.