Smart Driver Fatigue Detection System Using CNN

Abstract

Abstract: Road accidents frequently result from fatigued or sleepy drivers. More people die and are killed worldwide each year. Driver fatigue is a major factor in many accidents. It is currently one of the major causes of accidents. Numerous facial expressions, such as frequent yawning, frequent eye blinking, and head positioning, might indicate how tired someone is. Computer vision is the most suitable and practical technology to solve this issue since it uses these sensory properties. The suggested methodology employs Dlib, CNN, and OpenCV. Dlib estimates the positions of 68 coordinates (x,y) that help in mapping the facial points on a person’s face. The 2904 pictures that make up the collections classified are Open Eyes, Closed Eyes, Yawning,or No-Yawning. A convolutional neural network (CNN) will be used to determine the mouth and eye states from the Regionof Interest (ROI) photographs. The amount of mouth opening (FOM) and the percentage of eyelid closure (PERCLOS) overthe pupil are the measures used to measure weariness over time. The recommended system determines whether the driver is sleepyand sounds a warning when it detects their eyes and yawns fora certain amount of time. The accuracy of the model has been discussed.