  In industrial manufacturing, numerous tasks of visually inspecting or
detecting specific objects exist that are currently performed manually or by
classical image processing methods. Therefore, introducing recent deep learning
models to industrial environments holds the potential to increase productivity
and enable new applications. However, gathering and labeling sufficient data is
often intractable, complicating the implementation of such projects. Hence,
image synthesis methods are commonly used to generate synthetic training data
from 3D models and annotate them automatically, although it results in a
sim-to-real domain gap. In this paper, we investigate the sim-to-real
generalization performance of standard object detectors on the complex
industrial application of terminal strip object detection. Combining domain
randomization and domain knowledge, we created an image synthesis pipeline for
automatically generating the training data. Moreover, we manually annotated 300
real images of terminal strips for the evaluation. The results show the
cruciality of the objects of interest to have the same scale in either domain.
Nevertheless, under optimized scaling conditions, the sim-to-real performance
difference in mean average precision amounts to 2.69 % for RetinaNet and 0.98 %
for Faster R-CNN, qualifying this approach for industrial requirements.
