Conference paper
Vessel Classification Using A Regression Neural Network Approach*
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.
Language: | English |
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Publisher: | IEEE |
Year: | 2021 |
Pages: | 4480-4486 |
Proceedings: | 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems |
ISBN: | 1665417145 and 9781665417143 |
ISSN: | 21530866 and 21530858 |
Types: | Conference paper |
DOI: | 10.1109/IROS51168.2021.9636161 |
ORCIDs: | Andersen, Rasmus Eckholdt , Nalpantidis, Lazaros and Boukas, Evangelos |
Deep learning Drones Electronic ballasts Inspection Intelligent robots Neural networks condition monitoring convolutional neural nets corroded area deep learning (artificial intelligence) deep learning approach faster R-CNN network feature pyramid backbone high wear human surveyors inspection inspection process inspections marine vessels mechanical engineering computing objective assessment regression analysis regression head regression neural network approach shipping owners ships tanks (containers) vehicle dynamics vessel classification wear