A Deep learning project for detecting distracted drivers using the Distracted Driver Dataset.

Distracted driving is a major concern that significantly impacts road safety. This project aims to develop a solution for detecting distracted drivers using machine learning techniques.

The Distracted Driver Dataset, available on Kaggle, serves as the foundation for our project. The images can be downloaded from the below link
https://www.kaggle.com/competitions/state-farm-distracted-driver-detection

• Implemented prelimnary baseline methods using resnet50 and densenet121 pretrained model
• Autoencoder approach to get the image latent space and derive the most important characterstics of the image.
• Semantic Segmentation of the images
• Ensemble methods using pretrained models like VGGnet, Resnet and densenet.

Our models have achieved promising results in detecting distracted drivers. The ensembled method, combining VGGNET, DenseNet121, and ResNet12 models, has demonstrated the highest accuracy of 98%. These results showcase the effectiveness of our solution in addressing the issue of distracted driving.

This project is licensed under the MIT License.

We would like to express our gratitude to Kaggle for providing the Distracted Driver Dataset, which has been instrumental in the development of this project.