Teknofest Traffic Signs Object Detection Dataset and Pre-Trained Model by Yolov7

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Explore Dataset

Here are a few use cases for this project:

Autonomous Vehicle Navigation: The model could be used in self-driving cars to recognize and interpret traffic signs on the roads, enabling it to respond appropriately, such as stopping for a stop sign or slowing down for a speed limit alert.
Advanced Driver Assistance Systems (ADAS): Traditional vehicles can leverage this model to alert drivers in real-time about the current traffic signs, ensuring safer driving and fewer traffic violations.
Traffic Management Simulations: Authorities or researchers could use this model in traffic simulation environments to test new traffic regulation measures, understand driver behavior, or plan new road infrastructure.
Road Inspection and Maintenance: Road maintenance agencies could use the "Teknofest Traffic Signs" model to detect damaged or obscured signs that need repair or replacement, based on images captured by road surveillance cameras or drones.
Virtual Reality Driving Training: In driving simulations, this model can be used to simulate real-world road scenarios, allowing new drivers to practice responding to different traffic signs, aiding in their driving education and training.

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{
                            teknofest-traffic-signs_dataset,
                            title = { Teknofest Traffic Signs Dataset },
                            type = { Open Source Dataset },
                            author = { Yolov7 },
                            howpublished = { \url{ https://universe.roboflow.com/yolov7-xt1tv/teknofest-traffic-signs } },
                            url = { https://universe.roboflow.com/yolov7-xt1tv/teknofest-traffic-signs },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jan },
                            note = { visited on 2024-05-10 },
                            }