Learning to Identify Drilling Defects in Turbine Blades with Single Stage Detectors. (arXiv:2208.04363v1 [cs.CV])

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 …

arxiv cv defects identify learning stage