Identification of Biofouling on Submerged Surfaces: Image Analysis for Deep Learning-Based Approaches

Abstract

Biofouling, the phenomenon involving the growth and accumulation of marine organisms on submerged surfaces, poses a significant challenge for the conservation of underwater structures and historical artifacts. Its presence can accelerate material degradation and complicate potential restoration efforts, especially in cases where direct recovery from the marine environment is not feasible. In this context, the early identification of biofouling is crucial for developing targeted intervention strategies and minimizing long-term damage. This study aims to lay the groundwork for creating a training dataset for a deep learning-based segmentation network. Images of submerged specimens were captured in both the visible spectrum and under UV illumination, which reveals biofouling often invisible to the naked eye. Using semi-automatic thresholding methods, segmentation masks were generated from UV images to approximate biofouling regions. RGB, HSV, Lab, and YCbCr color spaces were systematically compared to determine the most stable and reliable color model for segmentation. Results identified the HSV space as offering the most consistent thresholding performance across the dataset. These segmentation masks provide a foundation for training deep learning models aimed at automatically detecting biofouling in visible-light images, where manual annotation is challenging.