A multi-task segmentation and classification network for remote ship hull inspection

A Deep Learning Image Corrosion Classification Method for Marine Vessels Using an Eigen Tree Hierarchy Module

“…In order to explore performance characteristics of the selected methods, we use the MaVeCoDD dataset (Last Accessed: 12 June 2024), which has been utilised in similar other studies (e.g., [22,23,26,27]), and comprises of corrosion images for marine vessels in dry-dock and moored conditions. The dataset incorporates several artifacts under various lighting conditions as well contrast (e.g., changing lighting conditions, sky, sea) and background complexities (e.g., objects in front of the hull: maintenance ladders, rudders), with the camera viewpoint set at various angles and distances from corrosion areas of interest.…”

“…This is mainly due to the diverse geometrical shape, which makes it difficult to postulate prior knowledge on the basis of a generalised morphology. Recent advances in deep learning models [16,17] have examined the applicability and potential of machine vision for the inspection of large structures [18][19][20] and segmentation of corrosion in marine vessels [2,11,[21][22][23].…”

“…As a result, these deep-learning-trained models produce many false positives. In particular, such deep learning models seem to exhibit a decreased accuracy in recognition when an increase in crack-like texture and/or locally dispersed illumination appear due to crack depth in the training images [2,12,23]. This surface texture produces high 'colour' similarity between groups of pixels that results in reduced precision and accuracy [12,21,22].…”