road cracks segmentation 2 Computer Vision Project

NENU

Updated 2 years ago

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Description

Here are a few use cases for this project:

  1. Infrastructure Maintenance: Cities or municipalities can use this model to analyze road quality locally, identifying areas that may need immediate repair or improvement.

  2. Safety Monitoring: Transportation authorities can use the dataset to monitor road safety, using it to alert to potential driving hazards and prioritize road fixings.

  3. Civil Engineering Research: Researchers in civil engineering could use the model to study road degradation over time and test the effectiveness of different materials or construction methods.

  4. Autonomous Vehicle Guidance: The data can be integrated into autonomous (self-driving) vehicle systems to improve their ability to navigate and avoid potholes or cracks on roads, enhancing their safety measures.

  5. Insurance Claim Validation: Insurance companies could use this model to validate claims related to vehicle damage caused by poor road conditions. They can inspect the image of the said location to assess the situation.

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Cite This Project

LICENSE
CC BY 4.0

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

                        @misc{
                            road-cracks-segmentation-2_dataset,
                            title = { road cracks segmentation 2 Dataset },
                            type = { Open Source Dataset },
                            author = { NENU },
                            howpublished = { \url{ https://universe.roboflow.com/nenu/road-cracks-segmentation-2 } },
                            url = { https://universe.roboflow.com/nenu/road-cracks-segmentation-2 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { may },
                            note = { visited on 2025-02-03 },
                            }
                        
                    

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