Surface Defect Detection and Evaluation for Marine Vessels using Multi-Stage Deep Learning

TL;DR

This paper introduces a multi-stage deep learning pipeline for automated detection and evaluation of surface defects like corrosion and fouling on marine vessels, aiming to match expert assessments.

Contribution

It presents a novel multi-stage deep learning framework for automatic surface defect detection and measurement on ships, addressing variability in conditions and surface types.

Findings

Pipeline performs comparably to qualified inspectors

Effective segmentation of ship sections and defects

Accurate measurement of defect coverage percentages

Abstract

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.