Detecting Driver Drowsiness in Real Time Through Deep Learning Based Object Detection

Vehicle accidents due to drowsiness in drivers take thousands of lives each year worldwide. This fact clearly exhibits a need for a drowsiness detection application that can help prevent such accidents and ultimately save lives. In this work, we propose a novel deep learning methodology based on Convolutional Neural Networks (CNN) to tackle this problem. The proposed methodology treats drowsiness detection as an object detection task, and from an incoming video stream of a driver, detects and localizes open and closed eyes. MobileNet CNN architecture with Single Shot Multibox Detector (SSD) is used for this task of object detection. A separate algorithm is then used to detect driver drowsiness based on the output from the MobileNet-SSD architecture. In order to train the MobileNet-SSD Network a custom dataset of about 6000 images was compiled and labeled with the objects face, eye open and eye closed. Out of these, 350 images were randomly separated and used to test the trained model. The trained model was evaluated on the test dataset using the PASCAL VOC metric and achieved a Mean Average Precision (mAP) of 0.84 on these categories. The proposed methodology, while maintaining reasonable accuracy, is also computationally efficient and cost effective, as it can process an incoming video stream in real time on a standalone mobile device without the need of expensive hardware support. It can easily be deployed on cheap embedded devices in vehicles, such as the Raspberry Pi 3 or a mobile smartphone.