  Biofouling is a major challenge for sustainable shipping, filter membranes,
heat exchangers, and medical devices. The development of fouling-resistant
coatings requires the evaluation of their effectiveness. Such an evaluation is
usually based on the assessment of fouling progression after different exposure
times to the target medium (e.g., salt water). The manual assessment of
macrofouling requires expert knowledge about local fouling communities due to
high variances in phenotypical appearance, has single-image sampling
inaccuracies for certain species, and lacks spatial information. Here we
present an approach for automatic image-based macrofouling analysis. We created
a dataset with dense labels prepared from field panel images and propose a
convolutional network (adapted U-Net) for the semantic segmentation of
different macrofouling classes. The establishment of macrofouling localization
allows for the generation of a successional model which enables the
determination of direct surface attachment and in-depth epibiotic studies.
