Yolov5 Computer Vision Project
Updated 2 years ago
Here are a few use cases for this project:
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Traffic Management Systems: The computer vision model can be used in intelligent transportation systems to detect arrow signs for real-time traffic monitoring, traffic signal control activities, and driver assistance systems.
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Indoor Navigation Apps: The model can assist in developing mobile applications that guide users in crowded places like malls or airports by reading and interpreting arrow signs through real-time image recognition.
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Autonomous Vehicles: The model can be integrated into self-driving cars' systems to interpret road signs and navigate correctly, thus enhancing the vehicle's safety abilities.
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Construction Site Safety: The YOLOv5 model can be used to detect and interpret arrow signs at construction sites, thereby ensuring worker safety by effective communication of machinery movement or safety hazard areas.
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Accessibility Technology: This can also be used as an accessibility tool for individuals with visual impairments, aiding them in understanding their surroundings better by recognizing and interpreting arrow signs.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
yolov5-c2iji_dataset,
title = { Yolov5 Dataset },
type = { Open Source Dataset },
author = { Yolov5 Project },
howpublished = { \url{ https://universe.roboflow.com/yolov5-project-blfpt/yolov5-c2iji } },
url = { https://universe.roboflow.com/yolov5-project-blfpt/yolov5-c2iji },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2024-12-19 },
}