Domain adapted probabilistic inspection using deep probabilistic segmentation

Rasmus Eckholdt Andersen*, Evangelos Boukas

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

This paper introduces the concept of domain-adapted probabilistic segmentation for marine vessel classification. The evolution of corrosion is continuous and it is, therefore, impossible to acquire marine vessel inspection datasets representative of the entire active fleet. Additionally, human surveyors introduce high levels of subjectiveness in the classification process, resulting in potentially multiple equally valid but ambiguous classification results. Consequently, deterministic inspection is flawed. The goal of this paper is to address these challenges by using a probabilistic approach to segmentation while performing domain adaptation to align the feature space across the different stages of age degradation. We test a Probabilistic U-Net on both simulated images and images from real vessels and compare it against two novel probabilistic models. We have evaluated the models using both quantitative — energy distance as distribution similarity — and qualitative — feature reduction visualization — approaches. Our results indicate that the combination of probabilistic segmentation and domain adaption could potentially have a high impact on marine vessel surveys in the future.

Original languageEnglish
Article number113568
JournalOcean Engineering
Volume270
Number of pages9
ISSN0029-8018
DOIs
Publication statusPublished - 15 Feb 2023

Bibliographical note

Funding Information:
This work has been funded by the Innovation Fund Denmark (IFD) , through the Inspectrone (Autonomous and high-level commanded system for remote inspection of marine vessels to support classification and commercial operations) project, under contract number 8090-00080B .

Publisher Copyright:
© 2022 The Author(s)

Keywords

  • Domain adaptation
  • Marine vessels
  • Probabilistic segmentation

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