Universe

RoadDetection Computer Vision Model

byBlue HaloTask:
Instance Segmentation
License:CC BY 4.0154 views10 downloads

How to use the RoadDetection Segmentation API

Try This Model

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Code Snippets

from inference_sdk import InferenceHTTPClient

CLIENT = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key="API_KEY"
)

result = CLIENT.infer("YOUR_IMAGE.jpg", model_id="roaddetection-qenbs/1")
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Code
pip install inference-sdk
# 1. Import the library
from inference_sdk import InferenceHTTPClient

# 2. Connect to your workspace
client = InferenceHTTPClient(
  api_url="https://serverless.roboflow.com",
  api_key="API_KEY"
)

# 3. Run your workflow on an image
result = client.run_workflow(
  workspace_name="<YOUR_WORKSPACE>",
  workflow_id="<YOUR_WORKFLOW_ID>",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  parameters={
    "classes": "Road"
  },
  use_cache=True  # cache workflow definition for 15 minutes
)

# 4. Get your results
print(result)

Run on custom image

Drop an image here or click to upload

Detecting classes:
Or try a test image 
See all 1.3k imagesRoadDetection Road sampleRoadDetection Road sampleRoadDetection Road sampleRoadDetection Road sampleRoadDetection Road sampleRoadDetection Road sampleRoadDetection Road sampleRoadDetection Road sampleRoadDetection Road sampleRoadDetection Road sample showing RoadRoadDetection Road sample showing RoadRoadDetection Road sample

About RoadDetection Model

Here are a few use cases for this project:

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

Cite This Project

LicenseCC BY 4.0

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 2026-07-29 },
}

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