Hazmat Placards Object Detection Dataset and Pre-Trained Model by Digital ERG

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Explore Dataset

Here are a few use cases for this project:

Hazardous Material Management: This model could be used in an automated system for warehouses, shipping containers, or storage facilities to identify and track hazardous materials. It could also provide alerts for improper storage (e.g., flammable substances next to a heat source).
Emergency Services: Firefighters, police, and paramedics could use a 'Hazmat Placards' model to quickly identify the type of hazard they might encounter in an emergency situation, increasing safety and efficiency in response efforts.
Road Transport Safety: The model could be integrated in traffic monitoring systems to identify vehicles transporting hazardous materials. This information could be helpful in case of accidents, allowing for a fast and effective reaction.
Quality Assurance in Manufacturing: The model could be utilized on production lines of hazardous materials to ensure the correct labeling of products before they are sent out, mitigating risks associated with incorrect labeling.
Environmental Protection: Regulatory bodies can use it to ensure companies are correctly identifying and disposing of hazardous waste based on its classification, promoting a safer environment.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            hazmat-placards_dataset,
                            title = { Hazmat Placards Dataset },
                            type = { Open Source Dataset },
                            author = { Digital ERG },
                            howpublished = { \url{ https://universe.roboflow.com/digital-erg/hazmat-placards } },
                            url = { https://universe.roboflow.com/digital-erg/hazmat-placards },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jun },
                            note = { visited on 2024-07-27 },
                            }