Universe

Construction Computer Vision Model

byhnjnTask:
Object Detection
License:CC BY 4.0

How to use the Construction Detection 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="construction-fzkcd/2")
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Or, Use Free BrickworkAndMasonry, Completed and FoundationandFootings 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": "BrickworkAndMasonry, Completed, FoundationandFootings, InteriorWorkandFinishing, MEPandPlastering"
  },
  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 887 imagesConstruction SitePreparationExcavation sample showing FoundationandFootingsConstruction SitePreparationExcavation sample showing FoundationandFootingsConstruction SitePreparationExcavation sample showing StructuralFramingConstruction SitePreparationExcavation sample showing FoundationandFootingsConstruction SitePreparationExcavation sample showing FoundationandFootingsConstruction SitePreparationExcavation sample showing InteriorWorkandFinishingConstruction SitePreparationExcavation sample showing FoundationandFootingsConstruction SitePreparationExcavation sample showing MEPandPlasteringConstruction SitePreparationExcavation sample showing BrickworkAndMasonryConstruction SitePreparationExcavation sample showing FoundationandFootingsConstruction SitePreparationExcavation sample showing InteriorWorkandFinishingConstruction SitePreparationExcavation sample showing FoundationandFootings

About Construction Model

In large-scale construction projects, manual progress tracking is time-consuming and prone to human error. Our project leverages AI-driven image analysis to automatically classify construction site images into seven key stages, providing real-time progress monitoring. Using Faster R-CNN with ResNet-50-FPN, the system detects and categorizes construction activities, reducing the need for frequent site visits. This approach enhances project efficiency, accuracy, and decision-making, making it an essential tool for construction management.

Cite This Project

LicenseCC BY 4.0

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

@misc{ construction-fzkcd_dataset,
  title = { Construction Dataset },
  type = { Open Source Dataset },
  author = { hnjn },
  howpublished = { \url{ https://universe.roboflow.com/hnjn/construction-fzkcd } },
  url = { https://universe.roboflow.com/hnjn/construction-fzkcd },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2025 },
  month = { apr },
  note = { visited on 2026-07-29 },
}

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