CarsDataAnalysis2.0 Object Detection Dataset and Pre-Trained Model by Burgas Free University

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Explore Dataset

Here are a few use cases for this project:

Traffic Surveillance and Analysis: The CarsDataAnalysis2.0 model can be used to monitor traffic flow in cities by identifying specific makes and models. The data gathered can be used to optimize traffic management, analyze trends, and aid in transport planning.
Automated Parking Systems: The model can be integrated into smart parking systems to recognize car makes and models, allowing for tailored parking experiences and efficient management of parking spaces, as well as optimizing space allocation depending on vehicle sizes.
Enhanced Vehicle Security: CarsDataAnalysis2.0 can be used to identify stolen vehicles, unauthorized use of company vehicles, or illegally parked cars. Integration with security systems can help track, locate and secure vehicles faster.
Automotive Advertising and Targeted Marketing Campaigns: The model can help advertisers analyze images and videos from social media platforms, determining users' specific car preferences. Advertisers can then design targeted marketing campaigns catering to vehicle owners with personalized content based on their car make and model.
Insurance Claim Analysis and Fraud Detection: The model can be employed to support insurance claim investigations by accurately identifying vehicle makes and models from on-scene images or live stream footage. This can help detect fraudulent activities, such as insurance claim exaggeration or misrepresentation.

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{
                            carsdataanalysis2.0_dataset,
                            title = { CarsDataAnalysis2.0 Dataset },
                            type = { Open Source Dataset },
                            author = { Burgas Free University },
                            howpublished = { \url{ https://universe.roboflow.com/burgas-free-university/carsdataanalysis2.0 } },
                            url = { https://universe.roboflow.com/burgas-free-university/carsdataanalysis2.0 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { apr },
                            note = { visited on 2024-04-25 },
                            }