  Nondestructive testing (NDT) is widely applied to defect identification of
turbine components during manufacturing and operation. Operational efficiency
is key for gas turbine OEM (Original Equipment Manufacturers). Automating the
inspection process as much as possible, while minimizing the uncertainties
involved, is thus crucial. We propose a model based on RetinaNet to identify
drilling defects in X-ray images of turbine blades. The application is
challenging due to the large image resolutions in which defects are very small
and hardly captured by the commonly used anchor sizes, and also due to the
small size of the available dataset. As a matter of fact, all these issues are
pretty common in the application of Deep Learning-based object detection models
to industrial defect data. We overcome such issues using open source models,
splitting the input images into tiles and scaling them up, applying heavy data
augmentation, and optimizing the anchor size and aspect ratios with a
differential evolution solver. We validate the model with $3$-fold
cross-validation, showing a very high accuracy in identifying images with
defects. We also define a set of best practices which can help other
practitioners overcome similar challenges.
