TY - JOUR

T1 - Semantic segmentation for fully automated macrofouling analysis on coatings after field exposure

AU - Krause, Lutz M.K.

AU - Manderfeld, Emily

AU - Gnutt, Patricia

AU - Vogler, Louisa

AU - Wassick, Ann

AU - Richard, Kailey

AU - Rudolph, Marco

AU - Hunsucker, Kelli Z.

AU - Swain, Geoffrey W.

AU - Rosenhahn, Bodo

AU - Rosenhahn, Axel

N1 - Funding Information: The work was funded by ONR N00014-20-12244 (Ruhr University Bochum) and ONR N00014-20-1-2243 (Florida Institute of Technology). The authors are grateful to the Deutsche Forschungsgemeinschaft (DFG) for funding in GRK2376/331085229. The authors thank Labelbox for making their online annotation platform available to us.

PY - 2023

Y1 - 2023

N2 - 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 an approach for automatic image-based macrofouling analysis was presented. A dataset with dense labels prepared from field panel images was made and a convolutional network (adapted U-Net) for the semantic segmentation of different macrofouling classes was proposed. 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.

AB - 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 an approach for automatic image-based macrofouling analysis was presented. A dataset with dense labels prepared from field panel images was made and a convolutional network (adapted U-Net) for the semantic segmentation of different macrofouling classes was proposed. 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.

KW - deep learning

KW - environmental monitoring

KW - epibiotic analysis

KW - invasive species

KW - macrofouling

KW - ocean research

UR - http://www.scopus.com/inward/record.url?scp=85151168192&partnerID=8YFLogxK

U2 - 10.48550/arXiv.2211.11607

DO - 10.48550/arXiv.2211.11607

M3 - Article

C2 - 36924139

AN - SCOPUS:85151168192

VL - 39

SP - 64

EP - 79

JO - BIOFOULING

JF - BIOFOULING

SN - 0892-7014

IS - 1

ER -