Drowsiness Detection Computer Vision Project

achraf

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Description

Overview

The Drowsiness dataset is a collection of images of a person in a vehicle (Ritesh Kanjee, of Augmented Startups) simulating "drowsy" and "awake" facial postures. This dataset can easily be used as a benchmark for a "driver alertness" or "driver safety" computer vision model.

Example Footage!

Distracted Driver Model - Example Footage

Training and Deployment

The Drowsiness model has been trained with Roboflow Train, and available for inference on the Dataset tab. We have also trained a YOLOR model for robust detection and tracking of a fatigued driver. You can learn more here: https://augmentedstartups.info/YOLOR-Get-Started

About Augmented Startups

We are at the forefront of Artificial Intelligence in computer vision. With over 94k subscribers on YouTube, we embark on fun and innovative projects in this field and create videos and courses so that everyone can be an expert in this field. Our vision is to create a world full of inventors that can turn their dreams into reality.

Supervision

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Cite This Project

LICENSE
CC BY 4.0

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

                        @misc{
                            drowsiness-detection-cntmz-qxxod_dataset,
                            title = { Drowsiness Detection Dataset },
                            type = { Open Source Dataset },
                            author = { achraf },
                            howpublished = { \url{ https://universe.roboflow.com/achraf-7guqg/drowsiness-detection-cntmz-qxxod } },
                            url = { https://universe.roboflow.com/achraf-7guqg/drowsiness-detection-cntmz-qxxod },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { aug },
                            note = { visited on 2024-11-16 },
                            }
                        
                    

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