vechicle annotation Object Detection Dataset and Pre-Trained Model by graduation project

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Explore Dataset

Here are a few use cases for this project:

Traffic Analysis and Management: The "vehicle annotation" model can be utilized by transportation departments and city planners to analyze traffic patterns, congestion, and vehicle types in various locations. The data obtained can help optimize the implementation of road infrastructure, improve traffic flow, and adapt transportation policies accordingly.
Autonomous Vehicle Navigation: The model can assist self-driving vehicles in accurately identifying and differentiating between various vehicle types on the road. This allows the autonomous vehicle to adjust its speed, trajectory, and response based on the characteristics and potential behavior of the vehicles around it, enhancing safety and reliability.
Parking and Toll Collection: The "vehicle annotation" model can be integrated into parking management systems to automatically differentiate between vehicle types, enabling tailored parking policies and fees. Similarly, it can be used for implementing differential toll collection at toll booths based on the type of vehicle.
Surveillance and Security: Law enforcement and security agencies can deploy the model in CCTV camera systems to monitor and analyze the type of vehicles passing through specific areas of interest. This can help in the identification of stolen or suspicious vehicles, crime rate analysis, or efficient patrolling.
Augmented Reality Applications: Developers can incorporate the "vehicle annotation" model in AR applications to provide users with real-time information and recognition of various vehicle types, including technical specifications, market value, and even estimated environmental impact.

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{
                            vechicle-annotation_dataset,
                            title = { vechicle annotation Dataset },
                            type = { Open Source Dataset },
                            author = { graduation project },
                            howpublished = { \url{ https://universe.roboflow.com/graduation-project-cvgcw/vechicle-annotation } },
                            url = { https://universe.roboflow.com/graduation-project-cvgcw/vechicle-annotation },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { jul },
                            note = { visited on 2024-04-26 },
                            }