merged yolo Computer Vision Project
Updated 9 months ago
Here are a few use cases for this project:
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Autonomous Vehicles: This computer vision model would be very valuable for autonomous vehicles. The ability to identify different road objects is essential for safe navigation and decision-making in the driving process.
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Road Safety and Maintenance: The model could be used by city authorities or road maintenance organizations to identify potholes, obstructions, or other hazards on the road, making the streets safer and reducing maintenance timelines.
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Traffic Management Systems: This model could be used in traffic management systems to observe real-time traffic conditions by identifying vehicles, cycles, bikes, buses, and traffic lights, which could help in devising effective traffic control strategies.
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Augmented Reality Navigation Apps: The model could be embedded in navigation apps to provide an AR view of the user's surroundings, enhancing the user's understanding of the road conditions including nearby cyclists, pedestrians, animals, and the presence of signboards.
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Training Simulators for New Drivers: The model could be used in simulator software to recreate accurate and dynamic driving environments for individuals learning to drive, thereby making the learning experience more comprehensive and real-world applicable.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
merged-yolo_dataset,
title = { merged yolo Dataset },
type = { Open Source Dataset },
author = { BITS Pilani },
howpublished = { \url{ https://universe.roboflow.com/bits-pilani-fvdrw/merged-yolo } },
url = { https://universe.roboflow.com/bits-pilani-fvdrw/merged-yolo },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { feb },
note = { visited on 2024-11-23 },
}