batch_17 Computer Vision Project

group annotators two

Updated 3 years ago

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Classes (11)
fireHydrant
invalidSpot-Busstop
invalidSpot-entrance
invalidSpot-fireHydrant
invalidSpot-redCurb
invalidSpot-whiteCurb
invalidSpot-yellowCurb
spot-1
spot-2
spot-3
spot-4
Description

Here are a few use cases for this project:

  1. "Smart Parking" Mobile Application: Users can scan a road with their phone camera, and the app utilizing batch_17 computer vision model can identify available parking spots and categorize them according to type, helping the user to avoid illegal spots like entrances, fire hydrants, bus stops or forbidden colored curbs.

  2. City Planning: Urban planners can use batch_17 in analyzing recorded footage of cities' streets to understand how parking spots are utilized during the day, how many are invalid and why, offering data-driven insights to improve parking policies and design.

  3. Traffic Management System: Traffic officers may make use of batch_17 to automatically identify improperly parked cars or violations, such as cars parked in a fire hydrant area, bus stop, or on colored curbs, and take necessary actions accordingly.

  4. Autonomous Vehicles Navigation: Self-driving vehicles can utilize batch_17 to identify valid parking spots and also to avoid invalid spots during navigation. It will help them understand parking rules and adapt within city regulations.

  5. Real Estate Development: Real estate developers can use the batch_17 model to analyze potential areas for developments. It can identify the availability of parking spaces and their validity, which is an important metric to judge the desirability and potential value of a location.

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_17_dataset,
                            title = { batch_17 Dataset },
                            type = { Open Source Dataset },
                            author = { group annotators two },
                            howpublished = { \url{ https://universe.roboflow.com/group-annotators-two/batch_17 } },
                            url = { https://universe.roboflow.com/group-annotators-two/batch_17 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { jan },
                            note = { visited on 2024-11-22 },
                            }