  Deep learning is now the gold standard in computer vision-based quality
inspection systems. In order to detect defects, supervised learning is often
utilized, but necessitates a large amount of annotated images, which can be
costly: collecting, cleaning, and annotating the data is tedious and limits the
speed at which a system can be deployed as everything the system must detect
needs to be observed first. This can impede the inspection of rare defects,
since very few samples can be collected by the manufacturer. In this work, we
focus on simulations to solve this issue. We first present a generic simulation
pipeline to render images of defective or healthy (non defective) parts. As
metallic parts can be highly textured with small defects like holes, we design
a texture scanning and generation method. We assess the quality of the
generated images by training deep learning networks and by testing them on real
data from a manufacturer. We demonstrate that we can achieve encouraging
results on real defect detection using purely simulated data. Additionally, we
are able to improve global performances by concatenating simulated and real
data, showing that simulations can complement real images to boost
performances. Lastly, using domain adaptation techniques helps improving
slightly our final results.
