자율주행과 V2X통신을 활용한 스쿨존 안전 사고 예방 Object Detection Dataset and Pre-Trained Model by 22A1

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1180 images

Explore Dataset

Here are a few use cases for this project:

Autonomous School Zone Safety: This model can be applied to autonomous vehicles to help identify school zones and adjust driving behavior accordingly. It can potentially reduce the occurrence of accidents in school zones by correctly identifying and interacting with pedestrians, other cars, road markings, and traffic signals.
Traffic Safety Education: The model can be used for educational purposes. Demonstrations using this model can help to explain and visualize traffic rules and how they're being implemented in real-time to students and young drivers.
Predictive Analytics in Traffic Management: Government and transport management bodies could use this model to predict and manage traffic flow in and around school zones. Since the model can detect various factors such as pedestrians, cars, and road signs, predictive measures could be set in place to avoid congestions or accidents.
Traffic Surveillance Systems: The model could be utilized in traffic surveillance systems to closely monitor and enforce traffic rules within school zones. It can also be used to collect data for specific car classes, pedestrian colors, etc.
Automated Traffic Signal Adjustment: The technology can be used to automate traffic signal adjustments based on traffic flow and pedestrian activities in school zones. By identifying specific classes of vehicles or pedestrian colors, the system could effectively manage the traffic signals to ensure pedestrian safety and smooth traffic flow.

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{
                            -v2x_dataset,
                            title = { 자율주행과 V2X통신을 활용한 스쿨존 안전 사고 예방 Dataset },
                            type = { Open Source Dataset },
                            author = { 22A1 },
                            howpublished = { \url{ https://universe.roboflow.com/22a1/-v2x } },
                            url = { https://universe.roboflow.com/22a1/-v2x },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-06-22 },
                            }