Automated Image Analysis of Offshore Infrastructure Marine Biofouling
Automated Image Analysis of Offshore Infrastructure Marine Biofouling
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DOI：
10.3390/jmse6010002
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发表时间：
2018-03-01
影响因子：
2.9
通讯作者：
Scott, Beth E.
中科院分区：
文献类型：
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作者：
Gormley, Kate;McLellan, Faron;Scott, Beth E.
在英国，一些最古老的石油和天然气设施已经在水中存在了40多年，并受到海洋生物的大量殖民，这可能会导致行业挑战和/或潜在的生物多样性好处(例如人工珊瑚礁)。该项目的目标是对来自英国大陆架近海平台的海洋生物污损图像进行自动图像分析软件(CoralNet)的使用，目的是(1)培训该软件以查明联合王国平台上的主要海洋生物污损生物；(2)在3个不同的分析标准(方法A-C)下在3个平台上测试该软件的性能；(3)计算海洋生物污损生物的覆盖率；(4)向业界提供建议。经过对857幅图像的软件培训和对三个平台的测试，结果表明，三个平台的多样性从低(北海中部)到中等(北海北部)不等。北海中部的两个台地以羽毛海葵Metridium diantus为主，北部海蚀平台的物种优势不明显。建立了三种不同的分析标准，其中选择点的方法、评估的点数和可信度阈值(CT)各不相同：(方法A)随机选择20个点，CT为80%；(B)分层随机，50个点，CT为90%；(方法C)网格方法，100个点，CT为90%。在三个平台上进行的结果显示，大多数物种和比较对之间没有显著差异。在确认的注解方法(A、B和C)之间没有显著差异(在所有物种中)。认为该软件对北海主要污损物种的分类效果较好。总体而言，研究表明，使用自动图像分析软件可以更有效和一致地分析近海结构物上的海洋生物污垢；能够收集退役和其他作业行业的环境数据。
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.