Paper Key : IRJ************122
Author: M J Mohit Kumar,Monish M,Nandish N,Meka Sai Prasanna Kumar,Prasanna N
Date Published: 14 Jan 2023
Abstract
This paper presents a smartphone-based system for the detection of drowsiness in automotive drivers. The proposed framework uses three-stage drowsiness detection. The first stage uses the percentage of eyelid closure (PERCLOS) obtained through images captured by the front camera with a modified eye state classification method. The system uses near infrared lighting for illuminating the face of the driver during night driving. The second step uses the voiced to the unvoiced ratio obtained from the speech data from the microphone, in the event PERCLOS crosses the threshold. A final verification stage is used as a touch response within a stipulated time to declare the driver as drowsy and subsequently sound an alarm. The device maintains a log file of the periodic events of the metrics along with the corresponding GPS coordinates. The system has three advantages over existing drowsiness detection systems. First, the three-stage verification process makes the system more reliable. The second advantage is its implementation on an Android smart-phone, which is readily available to most drivers or cab owners as compared to other general purpose embedded platforms. This network is a combination of feature extraction and classifier modules. The feature extraction module uses the advantages of the standard convolution layers, depth wise separable convolution layers, average pooling layers, and proposed adaptive connections to extract the feature maps.
DOI LINK : 10.56726/IRJMETS32794 https://www.doi.org/10.56726/IRJMETS32794