[22기 A1조] 자율주행과 V2X통신을 활용한 스쿨존 안전사고 예방 Object Detection Dataset and Pre-Trained Model by 22A1

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Explore Dataset

Here are a few use cases for this project:

Autonomous Driving: Utilize this computer vision model for autonomous vehicles to identify elements such as cars, people, crosswalks, or school zone signs. This would allow the vehicle to react accurately and in real-time to its changing environment, thus enhancing the safety level of autonomous driving especially in school zones.
Traffic Monitoring Systems: Incorporate the model into traffic surveillance systems to identify and monitor real-time activities in school zones. This could provide up-to-date information on the number of pedestrians, specific types of cars, and adherence to school zone road markings, helping manage and prevent potential accidents.
Advanced Driver Assistance Systems (ADAS): This computer vision model could be used in ADAS to alert drivers in real-time about objects detected in school zones to increase pedestrian safety. Alerts could include proximity to crosswalks or presence of children with different color identifications.
Urban Planning: The model could be used to analyze traffic and pedestrian behavior in school zones, providing valuable data for urban planners when designing safer streets and infrastructure around school areas.
Safety Training Programs: Use this model within virtual reality-based driver's education or safety training programs to mimic real-life driving scenarios. Users can learn how to appropriately react when encountering different objects or persons in a school zone context.

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{
                            -22-a1-v2x_dataset,
                            title = { [22기 A1조] 자율주행과 V2X통신을 활용한 스쿨존 안전사고 예방 Dataset },
                            type = { Open Source Dataset },
                            author = { 22A1 },
                            howpublished = { \url{ https://universe.roboflow.com/22a1/-22-a1-v2x } },
                            url = { https://universe.roboflow.com/22a1/-22-a1-v2x },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-05-03 },
                            }