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.

Layer-by-Layer Deposited Hybrid Polymer Coatings Based on Polysaccharides and Zwitterionic Silanes with Marine Antifouling Properties.

Polyelectrolyte multilayer (PEM) assembly is a versatile tool to construct low-fouling coatings. For application in the marine environment, their structure needs to be stabilized by covalent linkage. Here, we introduce an approach for spin coating of silane-based sol-gel chemistries using layer-by-layer assembly of polysaccharide-based hybrid polymer coatings (LBLHPs).

Fully Convolutional Neural Network for Detection and Counting of Diatoms on Coatings after Short-Term Field Exposure.

While the use of deep learning is a valuable technology for automatic detection systems for medical data and images, the biofouling community is still lacking an analytical tool for the detection and counting of diatoms on samples after short-term field exposure. In this work, a fully convolutional neural network was implemented as a fast and simple approach to detect diatoms on two-channel (fluorescence and phase-contrast) microscopy images by predicting bounding boxes. The developed approach performs well with only a small number of trainable parameters and a F1 score of 0.