Vehicle_detection_yolov7 Object Detection Dataset and Pre-Trained Model by Thesis part 2

Images

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Explore Dataset

Here are a few use cases for this project:

Traffic Management Systems: The "Vehicle_detection_yolov7" computer vision model can be used to enhance traffic management systems by identifying various types of vehicles on the road. This data could further aid in traffic prediction, density estimation, and planning of infrastructure improvements.
Security and Surveillance: The model could be utilized in surveillance systems installed in cities or private properties to identify unauthorized or suspicious vehicle activities, or to aid in identifying and locating specific types of vehicles in case of emergencies or criminal investigations.
Autonomous Vehicle Technology: In the field of self-driving cars, this model can help in distinguishing between different types of vehicles and humans on the road. This can enhance the decision-making capacity of the autonomous vehicle for safer navigation.
Transportation Studies: Researchers and urban planners can use the detection model to conduct comprehensive studies on vehicle types and usage. This contributed data can help in understanding traffic patterns and planning for better urban development and public transportation strategies.
Emergency Response: The model can be used to quickly detect emergency vehicles like police cars or ambulances in traffic, allowing traffic control systems to proactively clear paths and reduce response times in critical situations.

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{
                            vehicle_detection_yolov7_dataset,
                            title = { Vehicle_detection_yolov7 Dataset },
                            type = { Open Source Dataset },
                            author = { Thesis part 2 },
                            howpublished = { \url{ https://universe.roboflow.com/thesis-part-2/vehicle_detection_yolov7 } },
                            url = { https://universe.roboflow.com/thesis-part-2/vehicle_detection_yolov7 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jun },
                            note = { visited on 2024-06-14 },
                            }