batch_13 Computer Vision Project

anntotators group one

Updated 3 years ago

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Classes (12)
Spot-1
Spot-2
Spot-3
Spot-4
Spot-5
fireHydrant
invalidSpot-entrance
invalidSpot-fireHydrant
invalidSpot-redCurb
invalidSpot-whiteCurb
invalidSpot-yellowCurb
reservedSpot-handicapped
Description

Here are a few use cases for this project:

  1. Smart Parking Management System: Utilize "batch_13" computer vision model to facilitate real-time monitoring and allocation of available parking spots in parking lots, garages, or on-street parking. The model can automatically identify available spots or reserved spots (like handicapped parking) and guide drivers to them, while avoiding invalid spots.

  2. Traffic Enforcement: Implement the model in traffic enforcement applications to monitor and identify vehicles parked in invalid spots, such as fire hydrants, red/yellow/white curbs, or entrances. This can help city officials issue timely citations and maintain orderly parking in urban areas.

  3. Parking Availability Mapper: Use "batch_13" model to develop an interactive map application that displays real-time parking availability for users. Drivers can use the application to quickly locate a valid parking spot nearby and avoid time-consuming searches.

  4. Assisted Parking for Autonomous Vehicles: Integrate the "batch_13" model into autonomous vehicle systems to aid in parking. The model will help autonomous vehicles accurately identify valid parking spots and avoid parking near fire hydrants, on colored curbs, or in restricted areas.

  5. Real-time Parking Usage Analysis: Employ the "batch_13" model for monitoring and analyzing overall parking spot usage patterns in specified areas over time. This information can aid urban planners and policy makers in making data-driven decisions about parking infrastructure development and improvements.

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_13_dataset,
                            title = { batch_13 Dataset },
                            type = { Open Source Dataset },
                            author = { anntotators group one },
                            howpublished = { \url{ https://universe.roboflow.com/anntotators-group-one/batch_13 } },
                            url = { https://universe.roboflow.com/anntotators-group-one/batch_13 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { jan },
                            note = { visited on 2024-11-24 },
                            }