batch_2 Computer Vision Project

anntotators group one

Updated 3 years ago

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Classes (10)
Spot-1
Spot-2
Spot-3
Spot-4
fireHydrant
invalidSpot-entrance
invalidSpot-fireHydrant
invalidSpot-redCurb
invalidSpot-yellowCurb
reservedSpot-handicapped
Description

Here are a few use cases for this project:

  1. Smart Parking Management: Utilize the "batch_2" model to streamline parking lot allocation by identifying valid, invalid, and reserved spots, and guiding drivers to available parking spaces in real-time.

  2. Parking Enforcement: Assist law enforcement and parking attendants in detecting parking violations by identifying vehicles parked in invalid spots (such as yellow or red curbs, fire hydrants, and entrances) or in reserved handicapped spaces without appropriate permits.

  3. Urban Planning and Development: Facilitate analysis of existing parking infrastructure by recognizing the distribution of parking spot types and identifying areas with overflow parking or high demand for specific parking types, such as handicapped spots.

  4. Accessibility Improvement: Identify areas that lack sufficient accessible parking spaces for disabled individuals, and provide data-driven recommendations to city planners for the addition of more reserved handicapped spots.

  5. Navigation and Map Applications: Integrate the "batch_2" model into mapping applications to provide users with real-time information on available parking spaces and restrictions, helping them quickly find suitable parking in urban areas.

Supervision

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Cite This Project

LICENSE
CC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

                        @misc{
                            batch_2-svzhq_dataset,
                            title = { batch_2 Dataset },
                            type = { Open Source Dataset },
                            author = { anntotators group one },
                            howpublished = { \url{ https://universe.roboflow.com/anntotators-group-one/batch_2-svzhq } },
                            url = { https://universe.roboflow.com/anntotators-group-one/batch_2-svzhq },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2021 },
                            month = { nov },
                            note = { visited on 2024-11-25 },
                            }