batch_7 Computer Vision Project

group annotators two

Updated 3 years ago

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Classes (12)
Invalidspot-busstop
Invalidspot-entrance
Invalidspot-fireHydrant
Invalidspot-fyreHydrant
Invalidspot-handicapped
Invalidspot-redcurb
Invalidspot-whitecurb
Invalidspot-yellowcurb
fireHydrant
spot-1
spot-2
spot-3
Description

Here are a few use cases for this project:

  1. Smart Parking Management: Utilize "batch_7" to analyze real-time images or video feeds of parking areas, instantly identifying valid and invalid parking spots to inform drivers and parking enforcement personnel about available spots and illegal parking instances.

  2. Municipal Parking Enforcement: Equip traffic law enforcement officers with devices that use "batch_7" to detect any vehicles parked in invalid spots, like fire hydrants, bus stops, or handicapped zones, and automatically issue parking citations.

  3. City Planning and Infrastructure Analysis: Integrate "batch_7" into urban planning software to analyze the layout of parking spots, assess the current distribution of valid and invalid spots in a city, and optimize the allocation of parking spaces to meet parking demands and regulations.

  4. Navigation and Parking Assistance: Integrate "batch_7" into vehicle navigation systems and smartphone apps to provide drivers with real-time updates on available valid parking spots nearby, allowing them to quickly find parking and avoid parking illegally.

  5. Research and Policy Development: Use "batch_7" to gather data on parking space violations in different regions, analyzing the patterns and frequency of illegal parking to inform policymakers in developing targeted solutions for improving parking infrastructure and compliance with parking regulations.

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_7_dataset,
                            title = { batch_7 Dataset },
                            type = { Open Source Dataset },
                            author = { group annotators two },
                            howpublished = { \url{ https://universe.roboflow.com/group-annotators-two/batch_7 } },
                            url = { https://universe.roboflow.com/group-annotators-two/batch_7 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2021 },
                            month = { dec },
                            note = { visited on 2024-11-25 },
                            }