emergency_vehicles_sans_domain Object Detection Dataset and Pre-Trained Model by emergencyvehicles

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Explore Dataset

Here are a few use cases for this project:

Traffic Monitoring Systems: The model can be applied to traffic monitoring cameras to accurately identify and track the movement of various emergency vehicles. This could improve response times by dynamically adjusting traffic light sequences or alerting other motorists via mobile or navigation platforms.
Emergency Response Analysis: Analyzing response times and effectiveness of emergency services. The model could track the dispatch, transit, and arrival of specific types of emergency vehicles, providing data to help analyze and optimize emergency response strategies.
Public Safety Applications: The model can be used in public surveillance systems to flag emergency situations by identifying the presence of emergency vehicles, allowing for faster human response or automation of emergency procedures.
Autonomous Vehicle Navigation: Self-driving cars could use this model to accurately detect and classify different types of emergency vehicles in various environmental conditions (day, night, high haze, motion blur etc.) and respond appropriately – e.g., it might need to yield to an ambulance but not necessarily to a police off-road vehicle not in active use.
Media Video Analysis: News agencies or public service departments could use the model to identify and categorize incidents involving emergency vehicles in their video footage, even in lower quality recordings.

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{
                            emergency_vehicles_sans_domain_dataset,
                            title = { emergency_vehicles_sans_domain Dataset },
                            type = { Open Source Dataset },
                            author = { emergencyvehicles },
                            howpublished = { \url{ https://universe.roboflow.com/emergencyvehicles/emergency_vehicles_sans_domain } },
                            url = { https://universe.roboflow.com/emergencyvehicles/emergency_vehicles_sans_domain },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { mar },
                            note = { visited on 2024-04-29 },
                            }