Keep Your AI-es on the Road: Tackling Distracted Driver Detection with Convolutional Neural Networks and Targeted Data Augmentation

TL;DR

This paper develops a robust multi-class classifier using CNNs and targeted data augmentation techniques to improve distracted driver detection accuracy, demonstrating a 15% F1 score increase over baseline models.

Contribution

It introduces a novel combination of pretrained models and augmentation methods, including skin segmentation and facial blurring, to enhance distracted driver detection performance.

Findings

15% increase in F1 score with combined augmentation techniques

Skin segmentation and facial blurring improve model robustness

Effective use of pretrained models and classical augmentation

Abstract

According to the World Health Organization, distracted driving is one of the leading cause of motor accidents and deaths in the world. In our study, we tackle the problem of distracted driving by aiming to build a robust multi-class classifier to detect and identify different forms of driver inattention using the State Farm Distracted Driving Dataset. We utilize combinations of pretrained image classification models, classical data augmentation, OpenCV based image preprocessing and skin segmentation augmentation approaches. Our best performing model combines several augmentation techniques, including skin segmentation, facial blurring, and classical augmentation techniques. This model achieves an approximately 15% increase in F1 score over the baseline, thus showing the promise in these techniques in enhancing the power of neural networks for the task of distracted driver detection.