Surface Defect Detection and Evaluation for Marine Vessels using Multi-Stage Deep Learning
This addresses the challenge of automating maintenance inspections for marine vessels, though it appears incremental as it builds on existing deep learning methods for a specific domain.
The paper tackled the problem of automating surface defect detection for marine vessels by developing a multi-stage deep learning pipeline, achieving results comparable to manual inspections by qualified inspectors.
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.