batch_14 Computer Vision Project

anntotators group one

Updated 3 years ago

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Classes (13)
fireHydrant
invalidSpot-Handicapped
invalidSpot-entrance
invalidSpot-fireHydrant
invalidSpot-redCurb
invalidSpot-whiteCurb
invalidSpot-yellowCurb
reservedSpot-handicapped
spot-1
spot-2
spot-3
spot-4
spot-5
Description

Here are a few use cases for this project:

  1. Smart Parking System: The model can help create a smart parking solution where users can check the availability of parking spots in real time. It can identify which spots are available (spot-1, spot-2, etc.), which are reserved for handicapped individuals, and which spots are invalid due to the presence of a fire hydrant or being near the entrance or certain curbs.

  2. Traffic Management: Use the model for better traffic management to ensure vehicles are parked properly without violating rules. By identifying invalid spots like fire hydrants, entrance, different colored curbs etc, authorities can take action against those who violate parking rules.

  3. Urban Planning: City planners can utilize the model to analyze parking patterns and spot efficiencies in different time intervals, locations, etc. This can help in forecasting demand for parking slots and can assist in designing effective parking layouts in cities.

  4. Mobile Applications: Develop a mobile app for drivers that uses this AI to display available parking spots and guide them to the closest one, while avoiding poorly marked or prohibited areas, increasing overall efficiency and user convenience.

  5. Security and Surveillance: Implement in security systems to monitor if cars are parked in illegal or restricted areas such as fire hydrants, red/yellow/white curbs or handicapped spots without authorisation, thus enhancing the safety and orderliness in the particular area.

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_14_dataset,
                            title = { batch_14 Dataset },
                            type = { Open Source Dataset },
                            author = { anntotators group one },
                            howpublished = { \url{ https://universe.roboflow.com/anntotators-group-one/batch_14 } },
                            url = { https://universe.roboflow.com/anntotators-group-one/batch_14 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { jan },
                            note = { visited on 2024-11-24 },
                            }