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J. Mar. Sci. Eng. 2018, 6(1), 2; https://doi.org/10.3390/jmse6010002

Automated Image Analysis of Offshore Infrastructure Marine Biofouling

1
School of Biological Sciences, University of Aberdeen, Tillydrone Avenue, Aberdeen AB24 2TZ, UK
2
BMT Cordah Ltd., Broadfold House, Broadfold Road, Aberdeen AB23 8EE, UK
3
ECAP Consultancy Group, Coinachan, Spean Bridge PH34 4EG, UK
4
Scripps Institution of Oceanography, 9500 Gilman Dr, La Jolla, CA 92093, USA
5
Smithsonian Tropical Research Institute, Apartado 0843-03092, Panama
*
Author to whom correspondence should be addressed.
Received: 7 August 2017 / Revised: 28 November 2017 / Accepted: 12 December 2017 / Published: 3 January 2018
(This article belongs to the Special Issue Maritime Environment Monitoring)
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Abstract

In the UK, some of the oldest oil and gas installations have been in the water for over 40 years and have considerable colonisation by marine organisms, which may lead to both industry challenges and/or potential biodiversity benefits (e.g., artificial reefs). The project objective was to test the use of an automated image analysis software (CoralNet) on images of marine biofouling from offshore platforms on the UK continental shelf, with the aim of (i) training the software to identify the main marine biofouling organisms on UK platforms; (ii) testing the software performance on 3 platforms under 3 different analysis criteria (methods A–C); (iii) calculating the percentage cover of marine biofouling organisms and (iv) providing recommendations to industry. Following software training with 857 images, and testing of three platforms, results showed that diversity of the three platforms ranged from low (in the central North Sea) to moderate (in the northern North Sea). The two central North Sea platforms were dominated by the plumose anemone Metridium dianthus; and the northern North Sea platform showed less obvious species domination. Three different analysis criteria were created, where the method of selection of points, number of points assessed and confidence level thresholds (CT) varied: (method A) random selection of 20 points with CT 80%, (method B) stratified random of 50 points with CT of 90% and (method C) a grid approach of 100 points with CT of 90%. Performed across the three platforms, the results showed that there were no significant differences across the majority of species and comparison pairs. No significant difference (across all species) was noted between confirmed annotations methods (A, B and C). It was considered that the software performed well for the classification of the main fouling species in the North Sea. Overall, the study showed that the use of automated image analysis software may enable a more efficient and consistent approach to marine biofouling analysis on offshore structures; enabling the collection of environmental data for decommissioning and other operational industries. View Full-Text
Keywords: biofouling; artificial reefs; offshore infrastructure; automated image analysis; CoralNet biofouling; artificial reefs; offshore infrastructure; automated image analysis; CoralNet
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Gormley, K.; McLellan, F.; McCabe, C.; Hinton, C.; Ferris, J.; Kline, D.I.; Scott, B.E. Automated Image Analysis of Offshore Infrastructure Marine Biofouling. J. Mar. Sci. Eng. 2018, 6, 2.

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