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September 5, 2025Journal of Marine Science and EngineeringOpen Access

Deep Learning Approaches for Fault Detection in Subsea Oil and Gas Pipelines: A Focus on Leak Detection Using Visual Data

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Authors

VSViviane F. da SilvaUniversidade Federal do Rio de Janeiro TNTheodoro Antoun NettoUniversidade Federal do Rio de Janeiro BRBessie A. RibeiroUniversidade Federal do Rio de Janeiro

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Implication

This research demonstrates deep learning techniques for leak detection in subsea environments, suggesting significant improvements over traditional methods.

Key Points

Automated leak detection using deep learning models can improve safety and reduce environmental impact.
The best models achieved macro F1-scores above 0.80 during evaluation, indicating strong performance.
The research utilized synthetic images and real subsea data, showcasing robustness in complex settings.
Releasing a novel dataset contributes to advancing automated subsea inspection methods within the community.

Cite This Study

Silva et al. (2025) studied this question.

synapsesocial.com/papers/68bb4d106d6d5674bcd00697 https://doi.org/10.3390/jmse13091683

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Implication

This research demonstrates deep learning techniques for leak detection in subsea environments, suggesting significant improvements over traditional methods.

Deep Learning Approaches for Fault Detection in Subsea Oil and Gas Pipelines: A Focus on Leak Detection Using Visual Data

Discussion

Member takes

Implication

Key Points

Cite This Study

Deep Learning Approaches for Fault Detection in Subsea Oil and Gas Pipelines: A Focus on Leak Detection Using Visual Data

Discussion

Member takes

Implication

Key Points

Cite This Study

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