Intersection Object Detection Dataset and Pre-Trained Model by Thesis

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Explore Dataset

Here are a few use cases for this project:

Traffic Flow Analysis: The "Intersection" model could be used to monitor and analyze traffic flow at busy intersections. The model can detect various vehicle types and provide data that aids in understanding traffic patterns, congestions, peak hours, and more.
City Transportation Planning: The ability to distinguish between various classes of vehicles makes "Intersection" valuable for city planning departments. They can use the data from this model to make more informed decisions relating to road design, public transportation infrastructure, and vehicular traffic regulation.
Autonomous Vehicle Development: "Intersection" can be a useful tool in developing smarter self-driving cars. The ability to correctly identify various vehicle types can inform decision-making algorithms used in autonomous vehicles, leading to safer and more efficient rides.
Vehicle Based Advertising: For advertising companies, the "Intersection" model can be used to assess the types of vehicles passing through a specific location. This data can guide strategies such as billboard placement or targeted advertisements, focusing on demographic profiles associated with certain vehicle types.
Crash Analytics and Insurance Risk Evaluation: Insurance companies could use the "Intersection" model to analyze accident-prone areas by identifying the types of vehicles typically involved. This information could assist in adjusting insurance premiums or identifying high-risk areas.

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{
                            intersection-t5pp2_dataset,
                            title = { Intersection Dataset },
                            type = { Open Source Dataset },
                            author = { Thesis },
                            howpublished = { \url{ https://universe.roboflow.com/thesis-8yadw/intersection-t5pp2 } },
                            url = { https://universe.roboflow.com/thesis-8yadw/intersection-t5pp2 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jun },
                            note = { visited on 2024-04-25 },
                            }