  In this work we propose a novel and fully automated method for extracting the
yarn geometrical features in woven composites so that a direct parametrization
of the textile reinforcement is achieved (e.g., FE mesh). Thus, our aim is not
only to perform yarn segmentation from tomographic images but rather to provide
a complete descriptive modeling of the fabric. As such, this direct approach
improves on previous methods that use voxel-wise masks as intermediate
representations followed by re-meshing operations (yarn envelope estimation).
The proposed approach employs two deep neural network architectures (U-Net and
Mask RCNN). First, we train the U-Net to generate synthetic CT images from the
corresponding FE simulations. This allows to generate large quantities of
annotated data without requiring costly manual annotations. This data is then
used to train the Mask R-CNN, which is focused on predicting contour points
around each of the yarns in the image. Experimental results show that our
method is accurate and robust for performing yarn instance segmentation on CT
images, this is further validated by quantitative and qualitative analyses.
