Hazard Sign Computer Vision Project

Start100

Updated 2 years ago

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7

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Classes (6)
Fair-Blocked
Fair-Dirty
Fair-Tilted
Good
Poor-Damaged
Poor-Old

Metrics

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Description

Here are a few use cases for this project:

  1. Road Maintenance Support: This model can assist maintenance crews by identifying damaged, blocked, tilted, or aged road signs, providing data for more efficient maintenance scheduling.

  2. Safety Analysis: The model can be used by traffic authorities or insurance companies to assess the potential safety risks around an area based on the condition of hazard signs. It can also be useful for accident investigation, identifying if poor signage conditions contributed to incidences.

  3. Autonomous Vehicle Matching: The model can support autonomous vehicle systems. These vehicles need to understand the state and meaning of each road sign for safe navigation, including those that may be faded, damaged, or obscured.

  4. Traffic Management: It can be helpful to traffic management authorities in deploying resources to clean or replace blocked, dirty or tilted signs to ensure smooth traffic flow and prevent accidents.

  5. Infrastructure Quality Monitoring: The model can be used by municipalities or city councils to monitor the quality of infrastructure, giving a quantitative measure of sign condition over time. This can provide valuable data in making decisions about infrastructure investments or upgrades.

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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{
                            hazard-sign-pmjzd_dataset,
                            title = { Hazard Sign Dataset },
                            type = { Open Source Dataset },
                            author = { Start100 },
                            howpublished = { \url{ https://universe.roboflow.com/start100/hazard-sign-pmjzd } },
                            url = { https://universe.roboflow.com/start100/hazard-sign-pmjzd },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { apr },
                            note = { visited on 2024-11-21 },
                            }