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Automated detection of sacrificial anodes on offshore wind farms for ROV inspections
Date
2024
Abstract
In this research, two transfer learning models were trained in order to detect sacrificial anodes on the base of a floating offshore wind turbine with the aim to aid the ROV pilot in navigation. Two models were trained on a dataset of sacrificial anodes collected by our team at WindFloat Atlantic windfarm in collaboration with OceanWinds. This data was then labelled and one model was trained and validated to detect the anodes. This workflow was then further tested in a tank with anodes manufactured from 3D printed materials to test the generalisability of the workflow using the second transfer learning model. The models performed well, one achieving recalls of 85% and the other demonstrating robustness by detecting anodes in the tank despite their vastly different appearance and absence from the training data.
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Description
Publisher
Institute of Electrical and Electronics Engineers
Citation
OCEANS 2024 - Halifax, Halifax, NS, Canada, 2024, pp. 1-6,
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Funding Information
This material is based on works supported by the Marine Institute Cullen Scholarship (grant number CS/22/008) and Marine Institute Networking Marine Research Communication Award grant Nº NET/24/063 , SEAI Research, Development and Demonstration Funding Programme 2021 (grant No.; 21/RDD/747); Science Foundation Ireland under the MaREI Centre research programme (grant Nº SFI/12/RC/2302P2, and SFI/14/SP/2740); and ATLANTIS project (grant Nº 871571). The data collection was in collaboration with OW Ocean Winds in WindFloat Atlantic windfarm.