  Biofouling is the accumulation of organisms on surfaces immersed in water. It
is of particular concern to the international shipping industry because it
increases fuel costs and presents a biosecurity risk by providing a pathway for
non-indigenous marine species to establish in new areas. There is growing
interest within jurisdictions to strengthen biofouling risk-management
regulations, but it is expensive to conduct in-water inspections and assess the
collected data to determine the biofouling state of vessel hulls. Machine
learning is well suited to tackle the latter challenge, and here we apply deep
learning to automate the classification of images from in-water inspections to
identify the presence and severity of fouling. We combined several datasets to
obtain over 10,000 images collected from in-water surveys which were annotated
by a group biofouling experts. We compared the annotations from three experts
on a 120-sample subset of these images, and found that they showed 89%
agreement (95% CI: 87-92%). Subsequent labelling of the whole dataset by one of
these experts achieved similar levels of agreement with this group of experts,
which we defined as performing at most 5% worse (p=0.009-0.054). Using these
expert labels, we were able to train a deep learning model that also agreed
similarly with the group of experts (p=0.001-0.014), demonstrating that
automated analysis of biofouling in images is feasible and effective using this
method.
