Traffic Safety Object Detection Dataset and Pre-Trained Model by Project 1

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Explore Dataset

Here are a few use cases for this project:

Road Safety Monitoring: This model could be integrated into city CCTV systems to identify instances of road safety violations such as motorcyclists not wearing helmets or missing mirrors on bikes, enabling authorities to enforce traffic rules more effectively.
Insurance Claim Verification: Insurance companies could use this system to verify the facts in accidents by assessing if drivers involved were compliant with safety protocols like wearing full helmets, half helmets or if they were without a helmet, which might affect their claims.
Driver Education Programs: This could be used in driver education programs to show real-world examples of unsafe practices (like not using mirrors or helmet) and the potential consequences.
AI-based Traffic Management System: This model could be part of an intelligent traffic management system that uses live feed from streets to analyze the behavior of bikers on roads, and implement safety measures based on data trends.
Autonomous Vehicle Training: This technology could also form part of the training data set for autonomous vehicles, helping them understand and react to a variety of traffic situations involving bikes.

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{
                            traffic-safety_dataset,
                            title = { Traffic Safety Dataset },
                            type = { Open Source Dataset },
                            author = { Project 1 },
                            howpublished = { \url{ https://universe.roboflow.com/project-1-n8oyq/traffic-safety } },
                            url = { https://universe.roboflow.com/project-1-n8oyq/traffic-safety },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-04-29 },
                            }