Evolutionary Deep Learning Approach for Driver Drowsiness Detection Using CNN and Genetic Algorithm

Abstract: Deep learning has advanced significantly with the Convolutional Neural Network (CNN) and Genetic Algorithm (GA) designs, especially in image recognition and sequential data processing. Biometric data like heart rate, pulse waves, brain waves, and eye movements are the mainstays of traditional drowsiness detection techniques. The technology may identify tiny indicators of exhaustion, such as variations in eyelid movement, eye closure rates, and facial expressions, by examining real-time visual data from a driver's face and eyes. Furthermore, the technology ensures prompt intervention and improves driver safety by providing real-time voice alarms when it detects indicators of drowsiness. Thus, the combination of CNN and GA provides a very effective, scalable, and real-time way to avoid driving accidents caused by drowsiness.

Keywords: Convolutional Neural Network (CNN) , Genetic Algorithm (GA), Deep Learning, Real-Time MonitoringI .