Development of Drowsiness Detection System for Drivers using Haar Cascade Classifier and Convolutional Neural Network

Abstract

The use of the Convolutional Neural Network (CNN) method to recognize an object in an image that is not too complex from the background and fore-ground shows very good results. However, in the case of images with various and very complex objects, the CNN method produces a large number of fea-ture maps, sometimes even unnecessary regions of interest (ROI) are includ-ed as material for model training which results in a lot of noise. This results in high computational costs and inconsistencies in the prediction results. Therefore, a pre-processing stage is needed, such as determining the area of interest (ROI) on the object of interest and the optimal architecture of CNN. This study applies the Haar Cascade Classifier method to determine the ROI of the object of interest in the image and CNN with the modified vgg-16 model architecture to detect drowsiness in drivers based on facial images. Test results based on the method used show optimal performance on exper-iments at various epochs with the highest accuracy was achieved 96.72%.