Vessel Classification Using A Regression Neural Network Approach*

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Marine vessels are subject to high wear and tear due to the conditions they operate in. To reduce risk of failure during operation, vessels are inspected periodically every five years. These inspections are prone to high subjectiveness that makes them hard to reproduce for the shipping owners. The purpose of this paper is to present a regressor to a Faster R-CNN network that can help alleviate some of the subjective assessment currently performed by human surveyors by estimating the severity of a corroded area, autonomously using drones. A feature pyramid backbone is shared between the Faster R-CNN and the added regression head. The goal of the regressor is to introduce a more objective assessment of the vessel that gives a consistent output for a consistent input. The system is evaluated on a real dataset, acquired in ballast tanks and the experimental results indicate that our deep learning approach can be used to detect and quantify corroded areas during the inspection process of marine vessels.
Original languageEnglish
Title of host publicationProceedings of 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherIEEE
Publication date2021
Pages4480-4486
ISBN (Print)9781665417143
DOIs
Publication statusPublished - 2021
Event2021 IEEE/RSJ International Conference on Intelligent Robots and Systems - On-line event, Prague, Czech Republic
Duration: 27 Sep 20211 Oct 2021
https://www.iros2021.org/

Conference

Conference2021 IEEE/RSJ International Conference on Intelligent Robots and Systems
LocationOn-line event
Country/TerritoryCzech Republic
CityPrague
Period27/09/202101/10/2021
Internet address

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