Raspberry Pi Based Driver Drowsiness Detection System Using Convolutional Neural Network (CNN)

This paper presents the implementation of a drowsiness driving detection system using Raspberry Pi. Drowsy driving can be defined as a behavioral decline in driving skills. In this work, the Convolutional Neural Network (CNN) has been used to classify drowsiness symptoms such as blinking and yawning. A total of 1310 images were used to train the CNN architecture. A 4 -layer convolution filter has been added as a layer in this CNN architecture. Adam optimization algorithm was then used to train the CNN. A real time study on the effectiveness of this prototype was conducted on 10 individuals. This proposed system successfully demonstrates a classification accuracy rate between 80% and 98%. Other factors that can affect the rate of classification accuracy, such as camera distance from the driver and lighting factors, are also studied in this paper.