GPR_data Computer Vision Dataset
About GPR_data Dataset
Here are a few use cases for this project:
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Automated Quality Control Inspection: Industries that manufacture or use steel ribs could use the "GPR_data" model to automate the quality inspection process. The model could identify different classes of steel ribs, enabling rapid and efficient categorization and detection of any pieces that do not match specified quality standards.
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Construction Industry: In the construction sector, this model could help in identifying and sorting different types of steel ribs used for building structures. It would improve the speed and accuracy of finding the correct materials, reducing the time spent on manual sorting and potentially minimizing construction errors.
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Steel Rib Sales and Distribution: Companies involved in the sales and distribution of steel ribs can use the "GPR_data" model to manage their inventory more effectively. By accurately identifying the classes of steel ribs, they can better track their product availability, leading to more efficient operations and enhanced customer service.
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Mining Support Systems: In mining operations, steel ribs are used extensively to support underground excavations. The "GPR_data" model can help determine appropriate steel rib classes to use in different sections of a mine, minimizing the risk of collapse and ensuring worker safety.
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Steel Recycling Plant: A recycling plant handling steel materials can use the "GPR_data" model to sort the incoming scrap steel ribs by class. This would aid in predicting potential value and deciding the best recycling or repurposing methods. It might also enable more specific, potentially profitable selling strategies for sorted scrap steel.
Use Free Steel-rib Detection API
Powered by general detection model
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": "steel-rib"
},
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
Tell the agent what you want to build.
Cite This Project
LicenseCC BY 4.0If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{ gpr_data-nfekx_dataset,
title = { GPR_data Dataset },
type = { Open Source Dataset },
author = { SMART GIO LAB },
howpublished = { \url{ https://universe.roboflow.com/smart-gio-lab/gpr_data-nfekx } },
url = { https://universe.roboflow.com/smart-gio-lab/gpr_data-nfekx },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { jun },
note = { visited on 2026-07-29 },
}