  In this work, we present an application of domain randomization and
generative adversarial networks (GAN) to train a near real-time object detector
for industrial electric parts, entirely in a simulated environment. Large scale
availability of labelled real world data is typically rare and difficult to
obtain in many industrial settings. As such here, only a few hundred of
unlabelled real images are used to train a Cyclic-GAN network, in combination
with various degree of domain randomization procedures. We demonstrate that
this enables robust translation of synthetic images to the real world domain.
We show that a combination of the original synthetic (simulation) and GAN
translated images, when used for training a Mask-RCNN object detection network
achieves greater than 0.95 mean average precision in detecting and classifying
a collection of industrial electric parts. We evaluate the performance across
different combinations of training data.
