scholarly journals Driver Safety Development: Real-Time Driver Drowsiness Detection System Based on Convolutional Neural Network

2020 ◽  
Vol 1 (5) ◽  
Author(s):  
Maryam Hashemi ◽  
Alireza Mirrashid ◽  
Aliasghar Beheshti Shirazi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Detection System ◽  
Driver Safety ◽  
Drowsiness Detection ◽  
Driver Drowsiness

scholarly journals Driver Drowsiness Detection System using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 2270-2272
Author(s):  
Charan M
Keyword(s):  
Real Time ◽  
Web Application ◽  
Detection System ◽  
Smart Phone ◽  
Learning Approaches ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
The Face ◽  
Bounding Boxes ◽  
Traditional Image

We propose a Driver drowsiness detection system, the purposes of which are to prevent from dangerous cause and to avoid accidents. Since all the processes on image recognition performed on a smart phone, the system does not need to send images to a server and runs on an android smart phone in a real-time way. Automatic image-based recognition is a particularly challenging task. Traditional image analysis approaches have achieved low classification accuracy in the past, whereas deep learning approaches without human supervision real-time drowsiness detection. This model classifies whether the person’s eyes are opened or closed. To recognize the face, a user should have to adjust camera such a way that it covers his face first, and then the system starts recognition within the indicated bounding boxes. In addition, the system estimates the actions of the person. This recognition process is performed repeatedly about every second. We will implement this system as Web application effectively for real-time recognition.

Visual-based Real Time Driver Drowsiness Detection System Using CNN

2021 ◽  
Author(s):  
Jonathan Flores-Monroy ◽  
Mariko Nakano-Miyatake ◽  
Gabriel Sanchez-Perez ◽  
Hector Perez-Meana
Keyword(s):  
Real Time ◽  
Detection System ◽  
Drowsiness Detection ◽  
Driver Drowsiness

A novel convolutional neural network method for subject-independent driver drowsiness detection based on single-channel data and EEG alpha spindles

2021 ◽  
pp. 095441192110178
Author(s):  
Sara Houshmand ◽  
Reza Kazemi ◽  
Hamed Salmanzadeh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Driving Simulator ◽  
Single Channel ◽  
Reference Method ◽  
Eeg Signals ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Driving Scenario ◽  
Eeg Alpha

A significant number of fatal accidents are caused by drowsy drivers worldwide. Driver drowsiness detection based on electroencephalography (EEG) signals has high accuracy and is known as a reference method for evaluating drowsiness. Among brain waves, EEG alpha spindle activity is a silent feature of decreasing alertness levels. In this paper, based on the detection of EEG alpha spindles, a novel driver drowsiness detection method is presented. The EEG spindles were detected using Continuous Wavelet Transform (CWT) analysis and the Morlet function. To do so, the signal is divided into 30-s epochs, and the observer rating of drowsiness determines the drowsiness level in each epoch. Tests were conducted on 17 healthy males in a driving simulator with a monotonous driving scenario. The Convolutional Neural Network (CNN) is used for classifying EEG signals and automatically learns features of the early drowsy state. The subject-independent classification results for single-channel P4 show 94% accuracy.

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