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

TL;DR

This paper introduces an automatic, image-based macrofouling analysis method using a convolutional neural network to improve the evaluation of fouling-resistant coatings after field exposure, providing spatial and detailed classification.

Contribution

It presents a novel deep learning approach with a dense-labeled dataset for semantic segmentation of macrofouling, enabling more accurate and spatially detailed assessments.

Findings

Successful development of a dense-labeled macrofouling dataset

Effective adaptation of U-Net for macrofouling segmentation

Enhanced spatial analysis of macrofouling progression

Abstract

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.