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Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

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Automatic Annotation of Subsea Pipelines using Deep Learning.

Anastasios Stamoulakatos¹, Javier Cardona¹, Chris McCaig¹

¹Department of Electronic and Electrical Engineering, University of Strathclyde Glasgow, UK.

Sensors (Basel, Switzerland)

|January 30, 2020

Summary

This summary is machine-generated.

This study introduces an automated image annotation framework for sub-sea oil and gas pipeline inspections. The system uses a Deep Convolutional Neural Network to classify critical events, improving efficiency and reducing costs.

Keywords:

deep learning multi-label image classification sub-sea pipeline survey transfer learning visual inspection

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Area of Science:

Marine Engineering
Artificial Intelligence
Asset Integrity Management

Background:

Sub-sea oil and gas pipeline inspections are mandated by regulations to monitor asset degradation.
Current inspection methods rely on Remotely Operated Vehicles (ROVs) and manual data annotation, which is time-consuming and expensive.

Purpose of the Study:

To develop an automated image annotation framework for sub-sea pipeline inspection videos.
To identify and classify key events such as exposure, burial, field joints, anodes, and free spans.

Main Methods:

Utilized transfer learning with a Deep Convolutional Neural Network (ResNet-50).
Fine-tuned the network on diverse sub-sea environmental data, including low lighting and marine growth.
Implemented a multi-label image classification approach for critical event detection.

Main Results:

Achieved high annotation performance, with accuracy ranging from 95.1% to 99.7% and F1-Scores from 90.4% to 99.4%.
Demonstrated the framework's capability for real-time annotation on a per-frame basis.
Validated the potential for significant cost reduction compared to manual annotation.

Conclusions:

The automated framework can significantly enhance the efficiency and cost-effectiveness of sub-sea pipeline inspections.
The developed algorithm can serve as a foundation for intelligent decision support systems in asset integrity management.
The system's performance in challenging sub-sea conditions highlights its practical applicability.