Colour-agnostic deep learning for automated biofouling assessment of antifouling coatings using soft probability labels

= 0.959 across 457 validation panels spanning five substrate colours; deployment on three unseen imaging sources produced spatially coherent outputs without retraining.

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Publication Details

Journal
Biofouling
Published
2026-11-09
DOI
https://doi.org/10.1080/08927014.2026.2753639
Primary Topic
Marine Biology and Environmental Chemistry
Type
article
Field-Weighted Citation Impact
0.00
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article

Colour-agnostic deep learning for automated biofouling assessment of antifouling coatings using soft probability labels

Kim Dam‐Johansen, J. Anani, C. Schmitz, J. A. H Dreyer et al.
Biofouling
Marine Biology and Environmental Chemistry
article

Colour-agnostic deep learning for automated biofouling assessment of antifouling coatings using soft probability labels

Kim Dam‐Johansen, J. Anani, C. Schmitz, J. A. H Dreyer, G. Zhang
article en

Abstract

= 0.959 across 457 validation panels spanning five substrate colours; deployment on three unseen imaging sources produced spatially coherent outputs without retraining.

Biofouling
Hochschule Niederrhein (DE), Technical University of Denmark (DK)
Marine Biology and Environmental Chemistry
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Colour-agnostic deep learning for automated biofouling assessment of antifouling coatings using soft probability labels — Kim Dam‐Johansen, J. Anani, et al. · Biofouling (2026) | TGRS Research Map | TGRS