seguranca em pontes Instance Segmentation Dataset and Pre-Trained Model by Yasser Schuck Antonio Tuma

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Explore Dataset

Here are a few use cases for this project:

Bridge Safety Monitoring: The "seguranca em pontes" model can be utilized in real-time monitoring systems on bridges to detect instances of individuals jumping off, which could potentially save lives.
Infrastructural Maintenance: Applying this model could help engineers or other personnel monitor the physical status of safety barriers on bridges, ensuring they are well-maintained and are correctly in place.
Traffic Management: This application could include understanding pedestrian movement, potentially identifying cases of jaywalking or any other abnormal pedestrian behavior around the bridge.
Event Detection: By using the model, authorities could detect any unauthorized events or gatherings that might occur on the bridge, such as a large group of people congregating at certain spots which can lead to potential safety risks.
Incident Reporting: The model could be integrated into an automatic incident reporting system that sends alerts to local authorities when a potential safety violation or unusual behavior is detected, like people climbing/jumping barriers.

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{
                            seguranca-em-pontes_dataset,
                            title = { seguranca em pontes Dataset },
                            type = { Open Source Dataset },
                            author = { Yasser Schuck Antonio Tuma },
                            howpublished = { \url{ https://universe.roboflow.com/yasser-schuck-antonio-tuma/seguranca-em-pontes } },
                            url = { https://universe.roboflow.com/yasser-schuck-antonio-tuma/seguranca-em-pontes },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { mar },
                            note = { visited on 2024-04-25 },
                            }