Azadi-Khavaran Computer Vision Project
Updated 2 years ago
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Metrics
Here are a few use cases for this project:
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Infrastructure Maintenance: City governments or transport departments can leverage the "Azadi-Khavaran" model for road surface health assessments and identifying areas for road repair. The model may be linked with GIS systems to pinpoint exact locations of pavement distress.
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Construction Quality Assurance: Construction companies can use the model to assess the quality of newly paved streets or surfaces. The timely identification of rutting or corrugation can help avoid future costly repairs.
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Urban Planning: The model can provide valuable data for urban planning purposes. With the ability to identify pavement distresses, urban planners can make informed decisions regarding road infrastructure improvement and development.
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Autonomous Vehicles: Self-driving car companies can utilize the model in their sensor suite to detect road damages, helping to improve navigation, planning alternative routes, and ensuring safety.
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Mobile Application: A mobile application could use this model to help the public report infrastructure faults. Users can snap a photo of the street, and the application can then classify the type of distress and automatically report it to the appropriate municipality or city council.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
azadi-khavaran_dataset,
title = { Azadi-Khavaran Dataset },
type = { Open Source Dataset },
author = { Thesis },
howpublished = { \url{ https://universe.roboflow.com/thesis-x90jz/azadi-khavaran } },
url = { https://universe.roboflow.com/thesis-x90jz/azadi-khavaran },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { jan },
note = { visited on 2024-11-24 },
}