crak segmentation Instance Segmentation Dataset and Pre-Trained Model by instance segmentation

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Here are a few use cases for this project:

Infrastructure Maintenance: The model can be used by government agencies or private companies to assess the condition of roads, bridges, and buildings in real-time. Regular scans can help detect emerging cracks, and consequently, worrisome structural issues in their early stages - leading to preventive maintenance.
Construction Quality Assurance: Construction firms can use the model to check and ensure the integrity of their work. The model can be used to inspect walls, floors, and other structures for cracks that indicate possible construction faults.
Safety Inspections: The model can be useful for companies dealing with safety inspections, such as fire departments or safety regulators, to identify cracks in various types of infrastructure like pipelines, chemical plants, or nuclear facilities that may pose accident risks.
Geological Study: Geological or seismological researchers can use this model to identify and categorize cracks in geological structures for analysis, potentially aiding in predicting earthquakes or land shifts.
Art Restoration: Museums or art restoration firms can use the model to detect and monitor cracks in artwork over time, aiding in the preservation and restoration process.

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{
                            crak-segmentation_dataset,
                            title = { crak segmentation Dataset },
                            type = { Open Source Dataset },
                            author = { instance segmentation },
                            howpublished = { \url{ https://universe.roboflow.com/instance-segmentation-fxbem/crak-segmentation } },
                            url = { https://universe.roboflow.com/instance-segmentation-fxbem/crak-segmentation },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { oct },
                            note = { visited on 2024-05-04 },
                            }