Underwater_pipes_orginal_pictures Object Detection Dataset and Pre-Trained Model by UnderwaterPIPES

Images

7972 images

Explore Dataset

Here are a few use cases for this project:

Underwater Infrastructure Maintenance: The model can help identify and classify underwater pipes for maintenance and repair activities, allowing professionals to easily assess the condition and plan necessary repairs for underwater pipelines and infrastructure.
Environmental Research and Monitoring: The model can be used for assessing the impact of underwater pipes on the surrounding ecosystems and water quality. This would help environmental researchers understand the potential risks of pipe leaks or spills, and develop contingency plans.
Marine Construction Planning: The model can assist engineers in industry or urban development projects by providing them with a clear understanding of existing underwater pipe networks, allowing for better planning and design in areas such as ports, offshore facilities, or coastal development.
Disaster Response and Recovery: In the event of natural disasters like hurricanes or tsunamis, the model can aid in identifying damaged or displaced pipes, helping emergency response teams to prioritize their efforts and make informed decisions for recovery and rebuilding.
Leak Detection and Monitoring: By identifying and classifying underwater pipes, the model can facilitate the monitoring of pipe health to detect potential leaks, leading to timely interventions to minimize environmental and financial impacts.

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{
                            underwater_pipes_orginal_pictures_dataset,
                            title = { Underwater_pipes_orginal_pictures Dataset },
                            type = { Open Source Dataset },
                            author = { UnderwaterPIPES },
                            howpublished = { \url{ https://universe.roboflow.com/underwaterpipes/underwater_pipes_orginal_pictures } },
                            url = { https://universe.roboflow.com/underwaterpipes/underwater_pipes_orginal_pictures },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { dec },
                            note = { visited on 2024-05-01 },
                            }