ChargeUpObjectDetectionBoundingBox Object Detection Dataset and Pre-Trained Model by Harfangs 3117

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Explore Dataset

Here are a few use cases for this project:

Robotics Competition Assistance: The ChargeUpObjectDetectionBoundingBox model can be used by teams participating in FRC (FIRST Robotics Competition) to efficiently detect and manipulate game pieces in the 2022 competition, leading to better strategy execution and potentially higher scores.
Training and Simulation: Using the model, coaches and team members can develop computer simulations or training games to practice and improve their robot's programming and strategy execution without the need for physical game pieces.
Augmented Reality Applications: By integrating the ChargeUpObjectDetectionBoundingBox model into an AR application, fans, spectators, and team members can view an enhanced version of the competition, with bounding boxes around game pieces to provide real-time information on the positions of Cone, Cone Base, CUBE, and Cone Tip.
Automated Game Piece Management: Event organizers can use the model to monitor the placement and distribution of game pieces during the competition, allowing for automated repositioning and replenishment of game pieces as needed, ensuring a smooth and fair competition.
Game Analysis and Strategy Development: Teams can use the ChargeUpObjectDetectionBoundingBox model to analyze recorded matches, allowing them to study the position and movement of game pieces, better understand their own strategies, and develop new tactics for future competitions.

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.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            chargeupobjectdetectionboundingbox_dataset,
                            title = { ChargeUpObjectDetectionBoundingBox Dataset },
                            type = { Open Source Dataset },
                            author = { Harfangs 3117 },
                            howpublished = { \url{ https://universe.roboflow.com/harfangs-3117/chargeupobjectdetectionboundingbox } },
                            url = { https://universe.roboflow.com/harfangs-3117/chargeupobjectdetectionboundingbox },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { mar },
                            note = { visited on 2024-04-18 },
                            }