Semantic Segmentation for Fully Automated Macrofouling Analysis on Coatings after Field Exposure
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.
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