  Detecting and evaluating surface coating defects is important for marine
vessel maintenance. Currently, the assessment is carried out manually by
qualified inspectors using international standards and their own experience.
Automating the processes is highly challenging because of the high level of
variation in vessel type, paint surface, coatings, lighting condition, weather
condition, paint colors, areas of the vessel, and time in service. We present a
novel deep learning-based pipeline to detect and evaluate the percentage of
corrosion, fouling, and delamination on the vessel surface from normal
photographs. We propose a multi-stage image processing framework, including
ship section segmentation, defect segmentation, and defect classification, to
automatically recognize different types of defects and measure the coverage
percentage on the ship surface. Experimental results demonstrate that our
proposed pipeline can objectively perform a similar assessment as a qualified
inspector.
