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Fiber-shaped materials (e.g. carbon nano tubes) are of great relevance, dueto 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 ofregion-based convolutional neural networks (R-CNNs) to automate this task. MaskR-CNN, the most widely used R-CNN for semantic segmentation tasks, is prone toerrors when it comes to the analysis of fiber-shaped objects. Hence, a newarchitecture - FibeR-CNN - is introduced and validated. FibeR-CNN combines twoestablished R-CNN architectures (Mask and Keypoint R-CNN) and adds additionalnetwork 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.

FibeR-CNN: Expanding Mask R-CNN to Improve Image-Based Fiber Analysis