  Object Detection (OD) is an important computer vision problem for industry,
which can be used for quality control in the production lines, among other
applications. Recently, Deep Learning (DL) methods have enabled practitioners
to train OD models performing well on complex real world images. However, the
adoption of these models in industry is still limited by the difficulty and the
significant cost of collecting high quality training datasets. On the other
hand, when applying OD to the context of production lines, CAD models of the
objects to be detected are often available. In this paper, we introduce a fully
automated method that uses a CAD model of an object and returns a fully trained
OD model for detecting this object. To do this, we created a Blender script
that generates realistic labeled datasets of images containing the object,
which are then used for training the OD model. The method is validated
experimentally on two practical examples, showing that this approach can
generate OD models performing well on real images, while being trained only on
synthetic images. The proposed method has potential to facilitate the adoption
of object detection models in industry as it is easy to adapt for new objects
and highly flexible. Hence, it can result in significant costs reduction, gains
in productivity and improved products quality.
