Driver Drowsiness Detection Model Using Convolutional Neural Networks\n Techniques for Android Application

A sleepy driver is arguably much more dangerous on the road than the one who\nis speeding as he is a victim of microsleeps. Automotive researchers and\nmanufacturers are trying to curb this problem with several technological\nsolutions that will avert such a crisis. This article focuses on the detection\nof such micro sleep and drowsiness using neural network based methodologies.\nOur previous work in this field involved using machine learning with\nmulti-layer perceptron to detect the same. In this paper, accuracy was\nincreased by utilizing facial landmarks which are detected by the camera and\nthat is passed to a Convolutional Neural Network (CNN) to classify drowsiness.\nThe achievement with this work is the capability to provide a lightweight\nalternative to heavier classification models with more than 88% for the\ncategory without glasses, more than 85% for the category night without glasses.\nOn average, more than 83% of accuracy was achieved in all categories. Moreover,\nas for model size, complexity and storage, there is a marked reduction in the\nnew proposed model in comparison to the benchmark model where the maximum size\nis 75 KB. The proposed CNN based model can be used to build a real-time driver\ndrowsiness detection system for embedded systems and Android devices with high\naccuracy and ease of use.\n

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