Road Sign Detection Object Detection Dataset and Pre-Trained Model by Rivne State Humanitarian University Faculty of Mathematics and Information Science

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Explore Dataset

Here are a few use cases for this project:

Navigational Assistance: This model can be integrated into navigation applications or GPS systems to provide real-time information on the road signs that route encompasses. This allows users to have advanced information about the road ahead.
Autonomous Vehicles: Self-driving cars require continuous understanding of surroundings to operate efficiently and safely. The "Road Sign Detection" model can greatly contribute by enabling the vehicle to recognize and interpret a vast range of road signs. This ensures compliance with traffic rules and enhances road safety.
Virtual Driving Simulators: In creating driving simulators for training and testing purposes, the model can be applied to allow the virtual environment to recognize and respond to road traffic signs. It enhances the realism and effectiveness of such training platforms.
Road Planning and Management: Municipalities or transport bodies can use this model to map and monitor the placement and visibility of road signs. It can help to identify missing or obscured signs, and can contribute to effective traffic management and safety.
Enhanced Surveillance Systems: Road cameras equipped with this model can detect violations of traffic sign rules and record necessary data for law enforcement. For example, ignoring stop signs, exceeding speed limitations, etc.

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{
                            road-sign-detection-gmkcf_dataset,
                            title = { Road Sign Detection Dataset },
                            type = { Open Source Dataset },
                            author = { Rivne State Humanitarian University Faculty of Mathematics and Information Science },
                            howpublished = { \url{ https://universe.roboflow.com/rivne-state-humanitarian-university-faculty-of-mathematics-and-information-science/road-sign-detection-gmkcf } },
                            url = { https://universe.roboflow.com/rivne-state-humanitarian-university-faculty-of-mathematics-and-information-science/road-sign-detection-gmkcf },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { apr },
                            note = { visited on 2024-04-29 },
                            }