  Automated fiber placement (AFP) is an advanced manufacturing technology that
increases the rate of production of composite materials. At the same time, the
need for adaptable and fast inline control methods of such parts raises.
Existing inspection systems make use of handcrafted filter chains and feature
detectors, tuned for a specific measurement methods by domain experts. These
methods hardly scale to new defects or different measurement devices. In this
paper, we propose to formulate AFP defect detection as an image segmentation
problem that can be solved in an end-to-end fashion using artificially
generated training data. We employ a probabilistic graphical model to generate
training images and annotations. We then train a deep neural network based on
recent architectures designed for image segmentation. This leads to an
appealing method that scales well with new defect types and measurement devices
and requires little real world data for training.
