Road_Crack_Surfac Object Detection Dataset and Pre-Trained Model by CrackDetection

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Here are a few use cases for this project:

Infrastructure Monitoring and Maintenance: Government agencies or private contractors can use the Crack_Detection model to identify and monitor road cracks and potholes. This enables them to plan and prioritize infrastructure repair works, ensuring road safety and reducing the overall maintenance cost.
Traffic Management: Transportation authorities can utilize Crack_Detection to identify areas with road cracks that may pose potential hazards to drivers. They can reroute traffic, issue travel alerts, or initiate temporary road closures to ensure public safety.
Smart City Development: Crack_Detection can be integrated into a comprehensive smart city solution, combining road condition data with other variables like traffic density, pollution levels, and urban planning. This helps urban planners design more efficient and sustainable urban transportation networks.
Vehicle Damage Assessment: Insurance companies or fleet managers can use the Crack_Detection model to evaluate the impact of poor road conditions on vehicle wear and tear. This information can be used to adjust insurance premiums or maintenance schedules, reducing costs and ensuring smoother vehicle operation.
Autonomous Vehicle Navigation: Developers of self-driving vehicle technology can incorporate Crack_Detection into their systems, enabling vehicles to identify and avoid road cracks and potholes. This ensures a smoother and safer driving experience while prolonging the lifespan of vehicle components.

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{
                            road_crack_surfac_dataset,
                            title = { Road_Crack_Surfac Dataset },
                            type = { Open Source Dataset },
                            author = { CrackDetection },
                            howpublished = { \url{ https://universe.roboflow.com/crackdetection-fxmk2/road_crack_surfac } },
                            url = { https://universe.roboflow.com/crackdetection-fxmk2/road_crack_surfac },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { may },
                            note = { visited on 2024-06-18 },
                            }