RoadDetection Instance Segmentation Dataset and Pre-Trained Model by Blue Halo

Images

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Explore Dataset

Here are a few use cases for this project:

Autonomous Vehicle Navigation: This RoadDetection model could be utilized in autonomous vehicles helping them to correctly recognize and navigate roads ensuring safe and efficient travel. Keywords: autonomous vehicles, car navigation, self-driving, safety, transportation.
Virtual Reality Applications: The model could be used for creating realistic VR environments by detecting and classifying different types of roads, allowing for enhanced immersive experiences. Keywords: virtual reality, simulation, gaming, VR environment, immersive experience.
Infrastructure Planning and Development: Urban planners and civil engineers could take advantage of this model to plan for road repairs, expansions or new road constructions. This automatic detection of roads can provide a comprehensive view of the city's infrastructure. Keywords: urban planning, civil engineering, infrastructure development, road construction, city planning.
Traffic Management Systems: The model could be used in intelligent traffic management systems to monitor the condition of roads and to identify any unreported incidents or unexpected situations. Keywords: Traffic management, road monitoring, public safety, incident detection, traffic control.
Drone Navigation: In aerial photography or delivery by drones, the model can be used to recognize roads and identify safe and appropriate landing or travel paths to follow avoiding obstacles. Keywords: drone navigation, aerial photography, drone delivery, path planning, obstacle avoidance.

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{
                            roaddetection-qenbs_dataset,
                            title = { RoadDetection Dataset },
                            type = { Open Source Dataset },
                            author = { Blue Halo },
                            howpublished = { \url{ https://universe.roboflow.com/blue-halo/roaddetection-qenbs } },
                            url = { https://universe.roboflow.com/blue-halo/roaddetection-qenbs },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-05-07 },
                            }