Automated Image Analysis of Offshore Infrastructure Marine Biofouling

Kate Gormley (Corresponding Author), Faron McLellan, Christopher McCabe, Claire Hinton, Joseph Ferris, David I Kline, Beth Scott

Research output: Contribution to journalArticle

3 Citations (Scopus)
12 Downloads (Pure)

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
Original languageEnglish
Article number2
JournalJournal of Marine Science and Engineering
Volume6
Issue number1
DOIs
Publication statusPublished - 3 Jan 2018

Fingerprint

Biofouling
biofouling
image analysis
Image analysis
infrastructure
software
Industry
Reefs
Biodiversity
Offshore structures
industry
Testing
Fouling
artificial reef
offshore structure
decommissioning
fouling
method
continental shelf
colonization

Keywords

  • biofouling
  • artificial reefs
  • offshore infrastructure
  • automated image analysis
  • CoralNet

Cite this

Automated Image Analysis of Offshore Infrastructure Marine Biofouling. / Gormley, Kate (Corresponding Author); McLellan, Faron ; McCabe, Christopher ; Hinton, Claire ; Ferris, Joseph; Kline, David I; Scott, Beth .

In: Journal of Marine Science and Engineering , Vol. 6, No. 1, 2, 03.01.2018.

Research output: Contribution to journalArticle

Gormley, Kate ; McLellan, Faron ; McCabe, Christopher ; Hinton, Claire ; Ferris, Joseph ; Kline, David I ; Scott, Beth . / Automated Image Analysis of Offshore Infrastructure Marine Biofouling. In: Journal of Marine Science and Engineering . 2018 ; Vol. 6, No. 1.
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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",
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author = "Kate Gormley and Faron McLellan and Christopher McCabe and Claire Hinton and Joseph Ferris and Kline, {David I} and Beth Scott",
note = "Supplementary Materials: The following are available online at www.mdpi.com/2077-1312/6/1/2/s1 Acknowledgments: This project was funded by the Natural Environmental Research Council (NERC) project No.: NE/N019865/1. The authors would like to thank Melanie Netherway and Don Orr, from our project partner (company requested to remain anonymous) for the provision of survey footage and for supporting the project. In addition, many thanks to Oscar Beijbom, University California Berkley for providing guidance and support to the project. Additional thanks to Calum Reay, Bibby Offshore; George Gair, Subsea 7; and Alan Buchan, Wood Group Kenny for help with footage collection and for allowing us to host workshops with them and their teams, their feedback and insights were very much appreciated.",
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N1 - Supplementary Materials: The following are available online at www.mdpi.com/2077-1312/6/1/2/s1 Acknowledgments: This project was funded by the Natural Environmental Research Council (NERC) project No.: NE/N019865/1. The authors would like to thank Melanie Netherway and Don Orr, from our project partner (company requested to remain anonymous) for the provision of survey footage and for supporting the project. In addition, many thanks to Oscar Beijbom, University California Berkley for providing guidance and support to the project. Additional thanks to Calum Reay, Bibby Offshore; George Gair, Subsea 7; and Alan Buchan, Wood Group Kenny for help with footage collection and for allowing us to host workshops with them and their teams, their feedback and insights were very much appreciated.

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N2 - 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

AB - 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

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