Road_defects Object Detection Dataset and Pre-Trained Model by Defect Road detection

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Explore Dataset

Here are a few use cases for this project:

City Infrastructure Maintenance: Municipalities could use the "Road_defects" model to proactively identify road damages such as potholes, ruts, and various cracks. This could lead to quicker repair times, potentially preventing accidents and improving overall road safety.
Transportation and Logistics Companies: Companies in the transport sector could use the model to identify and avoid routes with significant road damage, ensuring smoother rides and reducing vehicle wear and tear.
Autonomous Vehicles: The model could be integrated into self-driving vehicle systems to improve their navigation abilities, identifying and avoiding road defects which could lead to accidents or damage.
Road Quality Research: Scientific and research institutions could use this model for studies related to road quality, causes of road deterioration, or efficiency of road repair methodologies.
Insurance Companies: Insurers could leverage the model to assess road conditions in relation to insurance claims, establishing if a reported accident may have been influenced by poor road conditions such as potholes or cracks.

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_defects-eif9i_dataset,
                            title = { Road_defects Dataset },
                            type = { Open Source Dataset },
                            author = { Defect Road detection },
                            howpublished = { \url{ https://universe.roboflow.com/defect-road-detection/road_defects-eif9i } },
                            url = { https://universe.roboflow.com/defect-road-detection/road_defects-eif9i },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { may },
                            note = { visited on 2024-04-29 },
                            }