vehicle detector Object Detection Dataset and Pre-Trained Model by augur robotics

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Explore Dataset

Here are a few use cases for this project:

Traffic Flow Management: The "Vehicle Detector" computer vision model can be applied to analyze traffic flow in real-time. The model can identify vehicles such as cars, buses, trucks, motorcycles, and even pedestrians, to provide insights about congestion points, peak times, and potential accident zones.
Smart Parking Applications: This model could be used for monitoring and managing parking facilities. By recognizing different types of vehicles and determining vacant parking spaces, it can significantly improve the efficiency of the parking system.
Surveillance and Security: The model can monitor roads, intersections, or other areas to identify unauthorized vehicles or suspicious activity. It can also alert operators to potential pedestrian-vehicle interaction risks.
Autonomous Vehicles: The "Vehicle Detector" model could be used to train self-driving cars to better understand their environment by accurately identifying various types of vehicles and people on the road.
Urban Planning: For developing or redesigning urban infrastructure, planners can use this model to understand the types and volume of vehicles using each roadway, helping them to design more efficient transportation routes.

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{
                            vehicle-detector-1l18y_dataset,
                            title = { vehicle detector Dataset },
                            type = { Open Source Dataset },
                            author = { augur robotics },
                            howpublished = { \url{ https://universe.roboflow.com/augur-robotics/vehicle-detector-1l18y } },
                            url = { https://universe.roboflow.com/augur-robotics/vehicle-detector-1l18y },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { nov },
                            note = { visited on 2024-04-27 },
                            }