DRIVER DROWSINESS DETECTION SYSTEMS

Volume: 10 Issue: 02 | Feb 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 152
DRIVER DROWSINESS DETECTION SYSTEMS
Supreetha Ganesh, Amit K B, Akif Delvi, C N Shreyas, Gagandeep K
Supreetha Ganesh, Dept. of Computer Science Engineering, K.S Institute of Technology, Karnataka, India
Akif Delvi, Dept. of Computer Science Engineering, K.S Institute of Technology, Karnataka, India
Amit K B , Dept. of Computer Science Engineering, K.S Institute of Technology, Karnataka, India
C N Shreyas , Dept. of Computer Science Engineering, K.S Institute of Technology, Karnataka, India
Gagandeep K , Dept. of Computer Science Engineering, K.S Institute of Technology, Karnataka, India
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Abstract - Drowsiness has significant contribution to the
accidents on road. Accurate measurement is required to track
the state of the driver. It has various shortcomings.
Convolutional neural networks(CNN) developed using Keras
were utilized to create the model that we employed. CNN is a
branch of deep neural networks that is appropriate for image
classification. It consists of many layers that include input,
output and hidden layers. The drowsiness detection systems
for drivers have the potential to greatly increasetrafficsafety
by warning drivers to stop or take breaks when they are in
danger of nodding off behind the wheel.
Key words: CNN (Convolutional NeuralNetworks),deep
neuralnetwork, drowsiness, python, jupyter.
1. INTRODUCTION
A system called driver drowsiness detection can tell when a
driver is getting fatigued or dozing off while operating a
vehicle. Drowsy driving has been linked to fatal accidents
and other traffic fatalities, thus this can be a serious safety
risk. Driver drowsiness can be identified using a variety of
methods, including:
1. Eye tracking: Some systems track the driver's eyes using
eye tracking technology.Thedrivercanbenoddingoffiftheir
eyes are closed or moving erratically.
2.Face-recognition technology: Some systems examine the
driver's facialexpressionstolookfortirednessindicators like
drooping eyelids or a slack jaw.
3. Monitoring of the driver'sheartrate:Somesystemsutilize
sensors to track the driver's heart rate and alarm them if it
drops below a predetermined level, which could be a sign
that they are nodding off.
4. Vehicle monitoring: Some systems keepaneyeonhow the
car is driving and search for indications that the driver may
not be fully awake, such as lane wandering or abrupt
changes in speed.
2. RELATED WORKS
LSTM-CNN Architecture for Human ActivityRecognition
The paper [1] proposes a model for the traditional pattern
recognition techniqueshave advanced significantly in recent
years. The use of deep learningtechnologies to understand
human activitiesinmobileandwearablecomputingscenarios
has drawn a lot ofinterest due to its growing acceptance and
success. A deep neural network with Convolutional layers
and long short-term memory (LSTM) was suggested in this
research. With just a few model parameters,thismodelcould
automaticallyextract activity featuresandcategorizethem.In
conclusion, the LSTM-CNN model consistently outperforms
those suggested in other research and exhibits sound
generalization.
A Real-Time Driving Drowsiness Detection Algorithm
With Individual Differences Consideration
The paper [2] proposes a development ofadrivingsleepiness
detection algorithm is crucial for enhancing traffic safety.
However, the majority of them are focused on developing an
all-encompassing sleepiness detection technique while
ignoring the variations among individual drivers. This study
suggests a real-time sleepiness detection method for drivers
that takes into account their unique driving styles. Finally ,to
develop a offline training module and online monitoring
module in the study, taking into account the individual
variations of the drivers. A specific driver- specific classifier
built on SVM is trained, and while driving, the per-trained
classifieris used to assess the condition of the driver's eyes.
Comprehensive Drowsiness Level Detection Model
Combining Multi-modal Information
The paper [3] proposes a drowsiness detection model that
can identify all levels ofdrowsiness, from weak to strong, is
presented in this study. This method is predicated on the
fundamental premise. First, it is assessed how sensitive the
posture index and other indices were to different degrees of
drowsiness. Then, to cover all stages of drowsiness, and
develop a drowsiness detection model by combining a
number of indices sensitive to both weak and strong
drowsiness. After drowsiness detection, future research will
concentrate on the creation of arousing and arousal-
maintenance systems. Thesuccessindetectingdrowsinessat
a variety of degrees, even light drowsiness, will make it
possible to design interfaces thatletuserschoosestimulithat
are best suited to their level of drowsiness and thesettingsin
which they aredriving.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Real-Time Driver-Drowsiness DetectionSystem Using
Facial Features
The paper [4] proposes a system the DriCare uses video
images to detect drivers' signs of tirednesswithoutusageof
any gadgets on human body. Additionally, based on 68
critical features, It creates a newdetectingalgorithmforface
regions. Then, assessment on the drivers' condition using
these facial areas. It takes the features from eyes and lips
and generates a warning to driver. Based on facial key
points, it defines the detection zones for the face. Due to its
rapid operation, DriCare works in real-time.
Real Time Driver Fatigue DetectionSystem Based On
Multi_Task CNN
The paper [5] proposes a model for Multi- tasking
Convolutiona Neural Network (ConNN) which is
suggested in this article to identify driver weariness and
drowsiness.When modelling a driver's behaviour, theeyes
and mouth are used. Driver wearinessis tracked through
changes to these traits. In contrast to studies in the
literature, the suggested Multi-task ConNN model now
simultaneously incorporates mouth and eye information.
Calculations of the durationand percentage of closed eyes
(PERCLOS) as well as the frequency and duration of
mouth and yawning sneezes are used toassess driverfatigue
(FOM). Threecategories in are used in this study to
categorize the driver's level of weariness. The study's ability
to build afaster and more effective system with justone
model rather than separately building models for two
different ConNNarchitectures is one of its strongest points.
Future work will add the head condition, which is just as
crucial as the eye and mouth conditions, and integrate the
system into an embedded system.
Driver’s Drowsiness Detection
The paper [6] states the advancement of technology overthe
last 50 years has given drivers a lot of support by ensuring
high levels of comfort and safety in their automobiles. Driver
weariness is one of the many possible causes of accidents,
and itwill be discussed and addressed issues in this paper.
This work, will employ powerful artificial intelligence-based
algorithms to identify driver exhaustion and the rate of
drowsiness. It suggests a method to identify driver tiredness
using artificial facial traits including eye closure, yawning,
and vertical distances between the eyes and mouth. The
method for driving drowsiness detection and driver rate of
drowsiness is proposed in thisresearchproject.Itinfersfrom
the data of 9 patients that decision tree and neural network
classifiers have produced superior results than linear SVM
and LDA for classifying the driver into sleepy and non-
drowsy. As previously mentioned, we have defined an
algorithm for the Rate of Drowsiness. Decisions could be
made using previous methodologies basedoncharacteristics
like eye blinks and ocular closure. It has taken into account
the subject's eyes and lips as features and employed
contemporary classifiers tocategorize the subject as drowsy
or not .Although the presented classifiers are capable of
producing results that are reasonable, there is still room for
improvement in their efficiency. By examining numerous
other classifiers, onecan use adrowsinessdetectionclassifier
thatis more reliable. The algorithm can still be enhanced by
conducting research on additional datasets to increase the
rate of drowsiness detection.
3. OBJECTIVES
The objective is that the driver drowsiness detection
system's goal is to help reduce accidents involving both
passengers and vehicles.
The primary objective of driver drowsiness detection is
to increase road safety by reducing accidents brought on by
drowsy driving. Motor vehicle collisions involving drowsy
drivers frequently result in severe injuries or fatalities. The
objectives include identifying drowsiness states,popping up
alerts,decreasing the crash risksandalsoenhancingthe road
safety.
4. METHODOLOGY
In this Python project, we'll use OpenCVto collect webcam
photos and feed them into a Deep Learning model that will
identify whether a person's eyes are "Open" or "Closed"
based on their position. For this Python project, the strategy
we'll employ is as follows:
Let's now examine our algorithm's operation step by step.
Step 1: Take an image as input from acamera.
Input is taken in the form of an image using camera. Hence,
an infinite loop is created to record every frame in order to
use the webcam.
Step 2:Create a region ofinterest(ROI)andidentifyfaces
in the image.
Since Mark the face with rectangular bound and then create
the region of interest(ROI).OpenCV algorithm for detection
of the object accepts grayscale imagesintheformof input, so
first convert the image to grayscale in order to trace out the
face in it.
To identify the items, colour information is not required. To
find faces, we'll use the haar cascade classifier.
Step 3: Identify the eyes using ROI andprovide the
information to the classifier.
The classifier intakes input data of eyes from ROI, which is
similar to that for finding face. Prior to detecting the eyes, a
cascade classifier is set for the left and right eyes.

Step 4: Whether the eyes are open or closed will be
classified by the classifier.
Now classifier finds the state of eye whether closed or not.
For forecasting the eye state, CNN classifier is utilized. The
colour image is first converted to grayscale. We then resize
the image to specified pixels in accordance with how our
model was trained.
Step 5:Calculate a score to find out if aperson is drowsy.
Find the score to determine the drowsiness . The score
indicate how long the eyes are closed. Increase the score if
both eyes are closed else decrease.
Fig 1 : Block Diagram of Drowsiness detection
1.CNN Architecture
CNN: Convolutional neural networks . These networks may
sound like an odd amalgam of biology, math, and computer
science with a dash of CS, but they have been some of the
most important developments in the area ofcomputervision
and image processing. The multi layer perceptron (MLP) is a
regularized variant of the Convolutional neural networks .
They were created based on how the neurons in the visual
cortex ofanimals function.
a. Convolution Layers
The input layer, the hidden layer, and the output layer make
up the convolution layers. While in neural networks every
input neuron is linked to the hidden layer below it, in CNN
only a small subset of input layer neurons are linked to
those in the hidden layer. The features of an input image can
be extracted with the aid of convolution layers. Additionally,
it is capable ofnumerous tasks including edge detection.
b. Pooling Layers
The major objective of the pooling layer is to extract
features; by doing so, it helps to reduce the size of the
representationand the parameters, making this model more
efficient. It also aids in minimizing over fitting.Poolinglayers
are new layers that areapplied in convolution layers. The
featuremaps' dimension is decreased using it.
c. Fully Connected Layers
A fully connected(FC) neural networkcanbeusedtoclassify
the data into distinct classes after the features have been
retrieved. SVM can also be used in place of fully connected
layers, although doing so results in an additional layer of
complexity. Completely interconnected layers to enable
training of the model. This layer contains the data that is
crucial to the input, and it produces aprobability that the
model is attempting toforecast.
5. APPLICATION REQUIREMENTS
TensorFlow
An open source library forAI&ML is calledTensorFlow.Deep
neuralnetworks can make extensive use of it to concentrate
on training and interference. It has a comprehensive set of
tools andlibraries,enablesacademicstoincludecutting-edge
technology in machine learning, and makes it simple for
developers to create and deploy applications that use
machine learning.
Numpy:
It is primarily intended for numerical computations.
Additionally, the Python programming language has a
package called Numpy. Multidimensional arraymetricsanda
substantial numberofhigh levelmathematicaloperationsare
defined using Numpy.
Keras:
It is the TensorFlow library's interface. It is expandable,
modular, and user-friendly. It supports other widely used
features such asdropout, batch normalization, and pooling.
Jupyter Notebook:
The basic objective of the open-source scientific computing
programme Jupyter Notebook is to mix equations, visuals,
and live code. It supports more than 40 programming
languages. The terms Julia, Python, and R are combined to
form the moniker Jupyter. While Anaconda comes
preinstalled, Jupyter is primarily designed for data science
and analytics applications. Data sets, such as visuals and
charts, are produced by modules like Matplotlib, Plotly, or
Bokeh in Anaconda.
Open CV:
OpenCV is an open-source library used for processing image
and computer vision. It has a significant part in real-time
applications, which is much needed in modern world
scenario.It is used to analyze images and films to find faces,
objects, and handwriting. Python has a ability to handle the
OpenCV when it is integrated with libraries like NumPy. This
is used to identify visual patterns and features.

6. CONCLUSION
A non-invasive system to localize the eyes and monitor
fatigue was developed. Information about the eyes position
is obtained through self-developed image processing
algorithm. During the monitoring, the system is able to
decide if the eyes are opened or closed. When the eyes have
been closed for too long, a warning signal is issued. In
addition, during monitoring, the system is able to
automatically detect any eye localizingerrorthatmighthave
occurred. In case of this type of error, the system is able to
recover and properly localize the eyes.
The following conclusions were made:
Image processing achieves highly accurate and reliable
detection of drowsiness.
Image processing offers a non-invasive approach to
detecting drowsiness without the annoyance and
interference.
A drowsiness detection system developed around the
principle of image processing judges the drivers alertness
level on the basis of continuous eye closures.
REFERENCES
[1]Kun Xia,Jianguang Huang And Hanyu Wang,”LSTM-CNN
Architecture for Human Activity Recognition”, University Of
Shanghai For Science And Technology, Shanghai 200093,
China, March 20, 2020.
[2]Feng You, Xiaolong Li, YunboGong, Haiwei Wang, ”AReal-
Time Driving Drowsiness Detection Algorithm With
Individual Differences Consideration”, School Of Civil
Engineering And Transportation, South China University Of
Technology,Guanzhou, 510640, China, December 10, 2019
[3]Mika Sunagawa, Shin-Ichi Shikii, Wataru Nakai, Makoto
Mochizuki,“Comprehensive Drowsiness Level Detection
Model Combining Multimodal Information”, Ieee Sensors
Journal, Vol 20, No. 7, April 1,2020
[4]Wanghua Deng, And Ruoxue Wu,”Real- Time Driver-
Drowsiness Detection System Using Facial Features”,School
Of Software, Yunnan University, Kunming 650000, China,
August 21, 2019
[5]Burcu Kir Savas, And Yasar Becerikli, ”Real Time Driver
Fatigue Detection System Based On Multi-Task
ConNN”,Computer Engineering Department, Kocaeli
University, Turkey, January 3,2020.

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