Lane Area Semantic Segmentation Computer Vision Project
Updated 2 years ago
Metrics
Here are a few use cases for this project:
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Autonomous Vehicle Navigation: The "Lane Area Semantic Segmentation" model can be used by self-driving cars to accurately identify and understand road lanes, helping them to navigate safely and efficiently.
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Traffic Management Systems: This AI model can be integrated into traffic management systems to monitor the use of road lanes. It can assist in understanding traffic flow and congestion patterns to improve road network efficiency and management.
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Road Infrastructure Analysis: Government and urban planning authorities can use this model to analyze the condition of road lanes, detect any irregularities or damages, assisting in proactive road maintenance and planning.
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Advanced Driver-Assistance Systems (ADAS): The model can be utilized in ADAS to provide drivers with important information about their current lane positioning, helping to prevent lane departure accidents.
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Gaming and Simulation: In realistic driving simulation games or training programs, this model could be implemented to create a more accurate representation of real-world road conditions.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
lane-area-semantic-segmentation_dataset,
title = { Lane Area Semantic Segmentation Dataset },
type = { Open Source Dataset },
author = { Demarcationbased Road Lane Segmentation },
howpublished = { \url{ https://universe.roboflow.com/demarcationbased-road-lane-segmentation/lane-area-semantic-segmentation } },
url = { https://universe.roboflow.com/demarcationbased-road-lane-segmentation/lane-area-semantic-segmentation },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2025-02-16 },
}