DDWS Object Detection Dataset and Pre-Trained Model by velabs

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Explore Dataset

Here are a few use cases for this project:

Road Safety Applications: The DDWS model can be used in vehicles' software systems to detect unsafe driving behaviors such as driving while drinking, texting, or talking on the phone. It can then initiate safety measures like alerting the driver or activating an auto-brake system.
Surveillance System: The model could be used in conjunction with traffic cameras to automatically detect drivers engaging in reckless activities like drinking while driving or texting. This data can then be used by law enforcement to issue warnings or fines and improve road safety.
Transportation Services: Taxi and rideshare services could utilize this model to ensure the safety and professionalism of their drivers. If a driver is detected engaging in any of these unsafe practices, the information could be used for training or disciplinary measures.
Traffic Accident Analysis: The DDWS model can be useful for insurance companies and traffic accident investigators to determine the causes of an accident. If the driver was engaged in any distracting activity, the model would serve as an effective tool in understanding culpability and liability.
Autonomous Vehicle Development: The model might be employed in the development of autonomous vehicles for understanding human driver behavior and situations which commonly lead to accidents, thus refining autonomous driving algorithms for safer navigation.

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{
                            ddws-icv9n_dataset,
                            title = { DDWS Dataset },
                            type = { Open Source Dataset },
                            author = { velabs },
                            howpublished = { \url{ https://universe.roboflow.com/velabs/ddws-icv9n } },
                            url = { https://universe.roboflow.com/velabs/ddws-icv9n },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { may },
                            note = { visited on 2024-06-18 },
                            }