NavigateIt Object Detection Dataset and Pre-Trained Model by ITESM

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Explore Dataset

Here are a few use cases for this project:

Advanced Driver-Assistance Systems (ADAS): This model can be used as part of an ADAS, enhancing the vehicle's perception capabilities. It could read and interpret signs, improving navigation accuracy and ensuring better road safety by offering real-time information on traffic conditions and road signs to the driver.
Navigational Apps: This model can be incorporated into navigational apps. It can identify road signs and provide real-time directives to users, potentially offering more dynamic, contextually aware directions than current GPS systems.
Autonomous Vehicles: The NavigateIt model may serve as a critical component for Autonomous vehicles. It can provide essential information on road signs and traffic conditions, helping the autonomous vehicle's decision-making process concerning speed adjustment, direction, and adherence to traffic rules.
Advanced Mapping Services: Mapping services could use this model to automatically update and correct their map databases, ensuring they include up-to-date traffic sign information.
Augmented Reality (AR) Applications: In an AR setting, the model can be used to provide context-based information to users. For instance, it could identify signs and inform walkers or cyclists about routes, potential hazards, or guidance based on the perceived traffic signs.

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.

YOLOv5

This project has a YOLOv5 model checkpoint available for inference with Roboflow Deploy. YOLOv5 is a proven and tested, production ready, 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{
                            navigateit_dataset,
                            title = { NavigateIt Dataset },
                            type = { Open Source Dataset },
                            author = { ITESM },
                            howpublished = { \url{ https://universe.roboflow.com/itesm-easou/navigateit } },
                            url = { https://universe.roboflow.com/itesm-easou/navigateit },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jun },
                            note = { visited on 2024-05-04 },
                            }