pothole_hump Object Detection Dataset and Pre-Trained Model by originaldataset

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Here are a few use cases for this project:

Road Maintenance Monitoring: Municipal authorities can use the model to quickly identify areas that require maintenance such as pothole repairs. This would automate and expedite the process of road quality assessment which is typically performed manually.
Traffic Management Systems: The model can be integrated into traffic management systems to collect data on road conditions. This can allow for more efficient planning and execution of traffic routes and detours, notably in response to detected pothole or hump locations.
Autonomous Vehicle Navigation: Autonomous driving systems can use this model to detect road conditions in real-time and navigate more effectively, avoiding potential pothole hazards or appropriately managing speed over humps.
Intelligent Mapping Services: GPS and mapping service providers can use the model to provide users with real-time updates on road conditions, alerting them to approaching speed humps or potholes and potentially improving route suggestions.
Bicycle Lane Planning: City planners could use the model to identify potholes or humps in potential areas for bicycle lanes. This would help ensure these lanes are safe and comfortable for cyclists.

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{
                            pothole_hump_dataset,
                            title = { pothole_hump Dataset },
                            type = { Open Source Dataset },
                            author = { originaldataset },
                            howpublished = { \url{ https://universe.roboflow.com/originaldataset/pothole_hump } },
                            url = { https://universe.roboflow.com/originaldataset/pothole_hump },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { nov },
                            note = { visited on 2024-05-08 },
                            }