Safety Object Detection Dataset and Pre-Trained Model by Khoirun Nisa

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Explore Dataset

Here are a few use cases for this project:

Construction Site Safety: This model can be used to ensure compliance with safety rules at construction sites. It can automatically detect whether workers on the ground are wearing mandated safety equipment such as helmets and vests, thereby preventing potential accidents.
Industrial Plant Monitoring: The system can monitor employees in power plants, factories, oil and gas facilities to ensure they are abiding by safety procedures, wearing their helmets and vests when needed.
Traffic Management: The model could be deployed at intersections or busy roads to identify motorcyclists or cyclists who are not wearing helmets, aiding law enforcement in promoting road safety.
Sport Training Camps: Involving risky sports like skateboarding, cycling, skiing etc., the model can check if participants are wearing safety equipment before commencing the activity, thus ensuring safety protocols are met.
Disaster Response: During rescue operations in disaster-stricken areas, the system can help identify rescuers without safety gear, assisting team leaders in maintaining the safety of their personnel.

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{
                            safety-3mk72_dataset,
                            title = { Safety Dataset },
                            type = { Open Source Dataset },
                            author = { Khoirun Nisa },
                            howpublished = { \url{ https://universe.roboflow.com/khoirun-nisa-qkuec/safety-3mk72 } },
                            url = { https://universe.roboflow.com/khoirun-nisa-qkuec/safety-3mk72 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { apr },
                            note = { visited on 2024-06-26 },
                            }