  New production techniques have emerged that have made it possible to produce
metal parts with more complex shapes, making the quality control process more
difficult. This implies that the visual and superficial analysis has become
even more inefficient. On top of that, it is also not possible to detect
internal defects that these parts could have. The use of X-Ray images has made
this process much easier, allowing not only to detect superficial defects in a
much simpler way, but also to detect welding or casting defects that could
represent a serious hazard for the physical integrity of the metal parts. On
the other hand, the use of an automatic segmentation approach for detecting
defects would help diminish the dependence of defect detection on the
subjectivity of the factory operators and their time dependence variability.
The aim of this paper is to apply a deep learning system based on Detectron2, a
state-of-the-art library applied to object detection and segmentation in
images, for the identification and segmentation of these defects on X-Ray
images obtained mainly from automotive parts
