Footpath Guide-2 Object Detection Dataset and Pre-Trained Model by Vendor

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Explore Dataset

Here are a few use cases for this project:

Urban Planning: City planners can use the "Footpath Guide-2" model to better understand how urban spaces, particularly footpaths, are used and navigated. By identifying obstacles, street elements, and pedestrian behaviors, the model can provide insights to improve urban design, footpath maintenance, and signage.
Autonomous Vehicle Navigation: This model can be used in autonomous or semi-autonomous vehicles to ensure safe navigation in urban environments. By recognizing footpaths, obstacles, pedestrians, and other street goods, it can help autonomous driving systems make precise decisions in real time.
Traffic Management and Enforcement: Authorities can use the model to monitor violations, such as vehicles on the footpath, illegal vendors obstructing the path, or improper disposal of garbage on streets. This could lead to more efficient enforcement of city ordinances and help improve traffic flow.
Accessibility Audits: Given its ability to identify obstacles and uneven paths, the model could be used to conduct digital accessibility audits of public spaces. It could highlight areas in need of repair or enhancement to ensure seamless access for those with mobility issues.
Augmented Reality Navigation: The model could be integrated into AR navigation apps. By identifying real-time street elements and conditions, the app could provide dynamic, augmented guidance to pedestrians, addressing potential obstacles or difficulties ahead.

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.

YOLOv8

This project has a YOLOv8 model checkpoint available for inference with Roboflow Deploy. YOLOv8 is a new state-of-the-art real-time object detection model.

Cite This Project

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

@misc{
                            footpath-guide-2_dataset,
                            title = { Footpath Guide-2 Dataset },
                            type = { Open Source Dataset },
                            author = { Vendor },
                            howpublished = { \url{ https://universe.roboflow.com/vendor/footpath-guide-2 } },
                            url = { https://universe.roboflow.com/vendor/footpath-guide-2 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { nov },
                            note = { visited on 2024-05-08 },
                            }