Bumpers Computer Vision Project
Here are a few use cases for this project:
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Autonomous Vehicle Navigation: The "Bumpers" computer vision model could be used to identify bots in a high traffic area to avoid collision and maintain safe navigation for self-driving vehicles.
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Industrial Robot Management: In a manufacturing setup with various types of autonomous bots, the model could be used to identify the classes of bots for proper task allocation and management.
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Bot-based Gaming: "Bumpers" could be applied in the gaming industry to create games based on real-time identification and interaction with different bot classes.
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Bot Surveillance Systems: The model could be integrated into security systems to monitor and identify unauthorized bot activity in both virtual and physical spaces.
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Robotic Process Automation: In industries utilizing RPA, "Bumpers" could identify and categorize different bots used in the automation process to analyze their efficiency or performance.
Trained Model API
This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.
YOLOv8
This project has a YOLOv8 model checkpoint available for inference with Roboflow Deploy. YOLOv8 is a new state-of-the-art real-time object detection model.
Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
bumpers-uvpro_dataset,
title = { Bumpers Dataset },
type = { Open Source Dataset },
author = { FRC },
howpublished = { \url{ https://universe.roboflow.com/frc-5mxme/bumpers-uvpro } },
url = { https://universe.roboflow.com/frc-5mxme/bumpers-uvpro },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { jan },
note = { visited on 2024-04-27 },
}
Connect Your Model With Program Logic
Find utilities and guides to help you start using the Bumpers project in your project.
