Classification of Potholes Computer Vision Model

byPothole DefectsTask:
Object Detection
License:CC BY 4.0919 views34 downloads

How to use the Classification of Potholes Detection API

Try This Model

Drop an image here or click to upload

Or try a test image 

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="classification-of-potholes/4")
Give your agent everything it needs

Or, Use Free Bowl Shaped Pothole, Delamination Pothole and Edge Break Pothole Detection API

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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": "Bowl Shaped Pothole, Delamination Pothole, Edge Break Pothole, Punch Out Pothole, Spalling Pothole"
  },
  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 

About Classification of Potholes Model

Here are a few use cases for this project:

  1. Road Maintenance: Public work departments and road agencies can use this model to identify, classify, and prioritize the repair of potholes, improving efficiency and road safety.

  2. Vehicle Safety: Automobile manufacturers or tech companies could integrate this model into vehicle systems to alert drivers about upcoming potholes, enhancing driving safety and preventing vehicle damage.

  3. Infrastructure Analysis: Urban city planners and engineers can use the model to assess the current condition of road infrastructure, aid in planning future road projects, and allocate repair resources more effectively.

  4. Autonomous Vehicles: Self-driving car systems could use the model to detect and navigate around potholes, contributing to the overall navigational and safety features of the vehicle.

  5. Ride-Sharing Apps: Companies like Uber or Lyft could make use of this model to provide safer and smoother rides to their customers by avoiding roads with severe potholes, thus enhancing the user's experience.

Cite This Project

LicenseCC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ classification-of-potholes_dataset,
  title = { Classification of Potholes Dataset },
  type = { Open Source Dataset },
  author = { Pothole Defects },
  howpublished = { \url{ https://universe.roboflow.com/pothole-defects/classification-of-potholes } },
  url = { https://universe.roboflow.com/pothole-defects/classification-of-potholes },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2023 },
  month = { aug },
  note = { visited on 2026-07-29 },
}

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