Señales de transito Object Detection Dataset and Pre-Trained Model by Tesis

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Explore Dataset

Here are a few use cases for this project:

Smart Traffic Management Systems: This model could be used to intelligently control traffic flow in real-time, adjusting signal timings based on traffic volume, pedestrian movements, and more.
Augmented Reality Navigation Apps: The model can enhance AR navigation applications by recognizing various signage and providing real-time updates or instructions to users, such as indicating when it's safe for pedestrians to cross or when a bus stop is nearby.
Autonomous Vehicles: Self-driving cars, buses, and transport vehicles could use this model to understand traffic rules and signals, making decisions on when to stop, slow down, or proceed with caution.
Assistive Technology for the Visually Impaired: The model could be integrated into aid systems (e.g. smart glasses) to help visually impaired individuals navigate urban environments by alerting them via audio cues about red/green lights, pedestrian crossings, and bus stops.
Urban Planning and Infrastructure Development: The model could be used to identify and map the locations of traffic signs and signals across a city, aiding in effective planning and improvement of road safety and infrastructure.

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{
                            senales-de-transito_dataset,
                            title = { Señales de transito Dataset },
                            type = { Open Source Dataset },
                            author = { Tesis },
                            howpublished = { \url{ https://universe.roboflow.com/tesis-lhuim/senales-de-transito } },
                            url = { https://universe.roboflow.com/tesis-lhuim/senales-de-transito },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { nov },
                            note = { visited on 2024-05-03 },
                            }