subsea_pipeline Object Detection Dataset and Pre-Trained Model by INDdetectionmodel

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Explore Dataset

Here are a few use cases for this project:

Pipeline Damage Assessment: This model can be utilized by oil and gas companies for routine inspection and monitoring of subsea pipelines. It can help them identify corrosion, cleanliness, and cracks in a timely manner, preventing potential leaks and failures.
Environmental Protection: Governments and environmental agencies can use this model to detect damages in underwater pipelines to prevent potential oil spill disasters, protecting marine life and ecosystems.
Maritime Infrastructure Maintenance: Shipping companies or port authorities could leverage the model to monitor and maintain underwater infrastructure, ensuring the safety of waterways.
Insurance Claim Validation: Insurance companies could apply the model to validate insurance claims related to subsea pipeline damages. It could help them confirm the class of damage, speeding up the claim process.
Scientific Research: Researchers in marine engineering or related fields could use the model to analyze how different environmental factors (like saltwater or algae) cause specific types of damage (like corrosions or cracks) to undersea pipelines.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            subsea_pipeline_dataset,
                            title = { subsea_pipeline Dataset },
                            type = { Open Source Dataset },
                            author = { INDdetectionmodel },
                            howpublished = { \url{ https://universe.roboflow.com/inddetectionmodel/subsea_pipeline } },
                            url = { https://universe.roboflow.com/inddetectionmodel/subsea_pipeline },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-06-19 },
                            }