  Synthetic data is being used lately for training deep neural networks in
computer vision applications such as object detection, object segmentation and
6D object pose estimation. Domain randomization hereby plays an important role
in reducing the simulation to reality gap. However, this generalization might
not be effective in specialized domains like a production environment involving
complex assemblies. Either the individual parts, trained with synthetic images,
are integrated in much larger assemblies making them indistinguishable from
their counterparts and result in false positives or are partially occluded just
enough to give rise to false negatives. Domain knowledge is vital in these
cases and if conceived effectively while generating synthetic data, can show a
considerable improvement in bridging the simulation to reality gap. This paper
focuses on synthetic data generation procedures for parts and assemblies used
in a production environment. The basic procedures for synthetic data generation
and their various combinations are evaluated and compared on images captured in
a production environment, where results show up to 15% improvement using
combinations of basic procedures. Reducing the simulation to reality gap in
this way can aid to utilize the true potential of robot assisted production
using artificial intelligence.
