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Related Experiment Video

Updated: Jul 19, 2026

Quantitatively Measuring In situ Flows using a Self-Contained Underwater Velocimetry Apparatus SCUVA

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Automated underwater imaging system for continuous biofouling assessment.

J Anani¹, K Dam-Johansen¹, J A H Dreyer¹

¹Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark.

|January 15, 2026

Summary

This study introduces an automated system for biofouling assessment, improving accuracy and efficiency. It uses daily underwater imaging and machine learning to track fouling progression, reducing manual labor and subjectivity in antifouling research.

Keywords:

Antifouling coatings biofouling monitoring image analysis marine biofouling underwater imaging

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Area of Science:

Marine Biology
Materials Science
Image Analysis

Background:

Traditional antifouling coating evaluations are labor-intensive and subjective.
Manual assessments have infrequent sampling intervals, potentially missing critical fouling developments.
Existing methods lack the accuracy and repeatability needed for effective antifouling research.

Purpose of the Study:

To develop and present a novel, automated biofouling assessment system.
To improve the accuracy, repeatability, and efficiency of biofouling monitoring.
To reduce subjectivity in antifouling coating evaluations.

Main Methods:

A custom underwater camera captures daily images for automated biofouling assessment.
Median stacking technique removes transient imaging artifacts for accurate attachment point analysis.
Multi-exposure fusion enhances image illumination, while machine learning (ilastik, random forest) segments and classifies biofouling.

Main Results:

The automated system enables accurate tracking of biofouling progression and attachment points.
Image analysis techniques successfully differentiate biofouling classes and monitor growth.
The system demonstrates improved accuracy, repeatability, and efficiency compared to traditional methods.

Conclusions:

The novel automated system significantly enhances biofouling assessment accuracy and efficiency.
This approach reduces subjectivity and labor, benefiting antifouling research and development.
Daily imaging and machine learning provide a robust method for monitoring marine biofouling.