  Non-destructive testing is a set of techniques for defect detection in
materials. While the set of imaging techniques are manifold, ultrasonic imaging
is the one used the most. The analysis is mainly performed by human inspectors
manually analyzing recorded images. The low number of defects in real
ultrasonic inspections and legal issues considering data from such inspections
make it difficult to obtain proper results from automatic ultrasonic image
(B-scan) analysis. In this paper, we present a novel deep learning Generative
Adversarial Network model for generating ultrasonic B-scans with defects in
distinct locations. Furthermore, we show that generated B-scans can be used for
synthetic data augmentation, and can improve the performance of deep
convolutional neural object detection networks. Our novel method is
demonstrated on a dataset of almost 4000 B-scans with more than 6000 annotated
defects. Defect detection performance when training on real data yielded
average precision of 71%. By training only on generated data the results
increased to 72.1%, and by mixing generated and real data we achieve 75.7%
average precision. We believe that synthetic data generation can generalize to
other challenges with limited datasets and could be used for training human
personnel.
