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Automatic Annotation of Subsea Pipelines using Deep Learning

Stamoulakatos, Anastasios, Cardona, Javier, McCaig, Chris, Murray, David, Filius, Hein, Atkinson, Robert, Bellekens, Xavier, Michie, Craig, Andonovic, Ivan, Lazaridis, Pavlos, and others. (2020) Automatic Annotation of Subsea Pipelines using Deep Learning. Sensors, 20 (3). Article Number 674. ISSN 1424-8220. (doi:10.3390/s20030674) (KAR id:79935)

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Regulatory requirements for sub-sea oil and gas operators mandates the frequent inspection of pipeline assets to ensure that their degradation and damage are maintained at acceptable levels. The inspection process is usually sub-contracted to surveyors who utilize sub-sea Remotely Operated Vehicles (ROVs), launched from a surface vessel and piloted over the pipeline. ROVs capture data from various sensors/instruments which are subsequently reviewed and interpreted by human operators, creating a log of event annotations; a slow, labor-intensive and costly process. The paper presents an automatic image annotation framework that identifies/classifies key events of interest in the video footage viz. exposure, burial, field joints, anodes, and free spans. The reported methodology utilizes transfer learning with a Deep Convolutional Neural Network (ResNet-50), fine-tuned on real-life, representative data from challenging sub-sea environments with low lighting conditions, sand agitation, sea-life and vegetation. The network outputs are configured to perform multi-label image classifications for critical events. The annotation performance varies between 95.1 and 99.7 in terms of accuracy and 90.4 and 99.4 in terms of F1-Score depending on event type. The performance results are on a per-frame basis and corroborate the potential of the algorithm to be the foundation for an intelligent decision support framework that automates the annotation process. The solution can execute annotations in real-time and is significantly more cost-effective than human-only approaches.

Item Type: Article
DOI/Identification number: 10.3390/s20030674
Subjects: Q Science > Q Science (General)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Moinul Hossain
Date Deposited: 03 Feb 2020 22:44 UTC
Last Modified: 16 Feb 2021 14:11 UTC
Resource URI: (The current URI for this page, for reference purposes)
Hossain, Md. Moinul:
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