  Fiber-shaped materials (e.g. carbon nano tubes) are of great relevance, due
to their unique properties but also the health risk they can impose.
Unfortunately, image-based analysis of fibers still involves manual annotation,
which is a time-consuming and costly process. We therefore propose the use of
region-based convolutional neural networks (R-CNNs) to automate this task. Mask
R-CNN, the most widely used R-CNN for semantic segmentation tasks, is prone to
errors when it comes to the analysis of fiber-shaped objects. Hence, a new
architecture - FibeR-CNN - is introduced and validated. FibeR-CNN combines two
established R-CNN architectures (Mask and Keypoint R-CNN) and adds additional
network heads for the prediction of fiber widths and lengths. As a result,
FibeR-CNN is able to surpass the mean average precision of Mask R-CNN by 33 %
(11 percentage points) on a novel test data set of fiber images.
