Label เอิร์น Computer Vision Project
Updated 3 years ago
Here are a few use cases for this project:
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Traffic Monitoring and Management: The "Label เอิร์น" model can be used to count and classify vehicles on roads and highways, providing traffic authorities with real-time information to optimize traffic lights and lane management.
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Smart Parking Solutions: This model can be applied in parking lot management to detect and classify vehicles entering and leaving parking lots. Based on a vehicle's dimensions, the system can suggest optimal parking spaces for better utilization.
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Insurance Claims Validation: Insurance companies can use the "Label เอิร์น" model to identify the type of vehicles involved in accidents, helping adjusters verify claims and estimate the costs of damage more accurately.
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Road Safety and Accident Analysis: By analyzing vehicle classes involved in accidents, the model can be used to inform urban planners about infrastructure improvements, such as dedicated lanes or traffic calming measures for specific vehicle types.
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Autonomous Driving: The "Label เอิร์น" model can enhance autonomous driving systems by allowing them to recognize and react to different vehicle classes, improving overall navigation and collision-avoidance performance.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
label-yeebg_dataset,
title = { Label เอิร์น Dataset },
type = { Open Source Dataset },
author = { Traffic },
howpublished = { \url{ https://universe.roboflow.com/traffic/label-yeebg } },
url = { https://universe.roboflow.com/traffic/label-yeebg },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { jan },
note = { visited on 2024-12-22 },
}