Name: segmentation Dataset
License: https://creativecommons.org/licenses/by/4.0/

Images

1480 images

Explore Dataset

Here are a few use cases for this project:

Infrastructure Maintenance: This model can be extremely useful in detecting and classifying cracks in different infrastructure forms like bridges, roads, buildings, etc. Ensuring timely repairs and maintenance can reduce potential accidents and enhance safety.
Aerospace Industry: The model may also be used in the aerospace industry to identify cracks in different parts of aircraft, like the fuselage or engine components. This can contribute to improving flight safety and prolonging the service life of the aircraft.
Auto Industry Quality Control: The model can be used in the auto industry for detecting cracks in vehicle components during the manufacturing process. Early detection can help ensure high-quality products and reduce recall costs.
Archaeological Preservation: The segmentation model can be used by archaeologists and museum curators to detect cracks in ancient artifacts and structures. This can help prevent further damage and aid in restoration and preservation efforts.
Energy Sector: In the energy sector, especially renewable energy like wind turbines or solar panels, the model could be used to check for cracks that might affect efficiency and safety. Identifying cracks early can help prevent expensive downtime and repairs.

Cite This Project

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

@misc{
                            segmentation-c64n2_dataset,
                            title = { segmentation Dataset },
                            type = { Open Source Dataset },
                            author = { ENIM },
                            howpublished = { \url{ https://universe.roboflow.com/enim-lmuhi/segmentation-c64n2 } },
                            url = { https://universe.roboflow.com/enim-lmuhi/segmentation-c64n2 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { may },
                            note = { visited on 2024-05-20 },
                            }