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According to the World Health Organization, distracted driving is one of theleading cause of motor accidents and deaths in the world. In our study, wetackle the problem of distracted driving by aiming to build a robustmulti-class classifier to detect and identify different forms of driverinattention using the State Farm Distracted Driving Dataset. We utilizecombinations of pretrained image classification models, classical dataaugmentation, OpenCV based image preprocessing and skin segmentationaugmentation approaches. Our best performing model combines severalaugmentation techniques, including skin segmentation, facial blurring, andclassical augmentation techniques. This model achieves an approximately 15%increase in F1 score over the baseline, thus showing the promise in thesetechniques in enhancing the power of neural networks for the task of distracteddriver detection.

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