Automated Identification of Road Identifications using CNN and Keras

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 01 | Jan 2023 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 724
Automated Identification of Road Identifications using CNN and Keras
Ms.V.Krishna Vijaya1, P.Phani Supriya2, N.Ruthvika 3 , K.Susruthi 4, V.Varsha5
1 Associate Professor Department of Information Technology, KKR & KSR Institute Of Technology And Sciences(A),
Guntur, India
2,3,4,5 Undergraduate Students ,Department of Information Technology , KKR & KSR Institute Of Technology And
Sciences(A), Guntur, India
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Abstract - Self-driving cars or Driverless cars are widely
used nowadays but the only difficult task with them is they
can’t detect traffic signs effectively. It is very important to
follow traffic signs and rules for road safety. To improve
their efficiency automatic detection of traffic sign
technology is needed. Although the existing one is working
nicely, we need one with more efficiency and accuracy. It is a
difficult task to detect defective and unclear images or signs.
To make it easy we propose a concept. Here we develop a
model to detect unclear and defective traffic signs using
CNN and keras. Our objective is to develop a model that
automatically detects traffic signs even though they are
unclear or tampered. Which can be used in self-driving cars.
There are several different types of traffic signs and each of
them have different meanings. So identifying and following
them is necessary to avoid accidents. Hence this model is
used to identify those traffic signs and make decisions
according to those signs in self driving cars.
Key Words: Autonomous cars, Traffic signs, GPUS,
ROCMS, AMD, Deep Learning, CNN, Keras, intervention,
booming, convolution filters, pooling.
1. INTRODUCTION
Proper installation of Traffic signs is necessary for
avoiding accidents. Autonomous cars or Self-driving cars
use a special mechanism for moving from one place to
another palace without human intervention and following
traffic signs correctly is a very big task for it. Autonomous
cars are a very profitable and booming business nowadays
and many companies are trying to produce them in more
effective ways. Recognizing and classifying traffic signs is
the main task for them. The already existing system for
recognizing traffic signs is implemented in deep learning
by using Py torch library. Py torch is a python open-source
library mainly used to implement machine learning
algorithms and for natural language processing. The main
problem with the existing system is that it can run only on
the Linux operating system as py torch is built of AMD
GPUS ’s with ROCMs support. Nowadays the most popular
operating systems are windows and Linux. Windows
operating system is built by using Intel GPUs which cannot
be supported by Py torch. So, we developed a model using
CNN which is a deep learning algorithm by using keras
library.
2. LITERATURE REVIEW
[1] Carlos Filipe Moura Paulo, “Detection and Recognition
of Traffic Signs” detection of traffic signs, pp. 4, sep.2007.
The authors, VISHAL KUMAR PAL and KSHITIJ JASSAL and
SAYED OMAR SADAT in the document explained that
recognition of traffic signs can be done by a machine
learning algorithm by using color analysis and shape
analysis. In their project they used North American and
European data sets for training and testing the model.
Here the main problem is with the different
[2] Zhang, Z.J.; Li, W.Q.; Zhang, D.; Zhang, W. A review on
recognition of traffic signs. Proceedings of the 2014
International Conference on E-Commerce, E-Business and
E- Service (EEE), Hong Kong, China, 1–2 May 2014; pp.
139–144. In this document the author proposed an
algorithm that is used for detecting different signs based
on sensor and vision-based recognition techniques using
CLSR method which is a traditional machine learning
based method. For classification of images, they used
feature extraction and feature reduction methods.
Achieving high accuracy is the main advantage of using
this method.
[3] Arunima Singh | Dr. Ashok Kumar Sahoo "Traffic Sign
Recognition" Published International Journal of Trend in
Scientific Research and Development (ijtsrd), ISSN: 2456-
6470, Volume-2 | Issue-4, June 2018, pp.122-126, URL:
The authors Arunima Singh and Ashok Kumar Sahoo
developed a model that can be used for recognizing the
traffic signs in India by using feature extraction techniques
like Scale Invariant Feature Transform (SIFT) and Support
Vector Machine (SVM). Key point localization and
Assignment of orientation and Key point Descriptor the
major steps of classification. The main advantage of this
approach is effectiveness of framework and classification
accuracy.
[4] Rubén Laguna*, Rubén Barrientos*, L. Felipe
Blázquez*, Luis J. Miguel**, “Traffic sign recognition
application based on image processing
techniques”,Preprints of the 19th World Congress the
International Federation of Automatic Control Cape Town,
South Africa, background of study on traffic signs, pp.104,

Aug 24-29, 2014. Ruben Laguna et al. suggested a method
to classify the traffic signs based on image classification
and regions of concentration. The author used a graphical
user interface (GUI) to interact with the users and along
with the output the graph plots of success rate and loss
function. The 91.1 % success rate is the main merit of this
approach. But due to low resolution and wrong
orientation of images it faces some difficulties while
detecting the images.
[5] Mahammad A. HANNAN1, Safat B. WALI*1, Tan J. PIN1,
Aini HUSSAIN1, Salina A. SAMAD1, “Traffic Sign
Classification based on Neural Network for Advanced
Driver Assistance System” classification of traffic signs,
pp.169, Nov 2014. For detection of traffic signs, the author
proposed a methodology which can be divided into 3
major parts: image pre-processing, feature extraction and
classification. The used data set has more than 300 train
images and 180 test images of traffic signs. Along with the
predicted output confusion matrix is also displayed. In this
approach it takes more time for normalization.
[6] Fleyeh, H., "Road and Traffic Sign Color Detection and
Segmentation - A Fuzzy Approach" Machine Vision
Applications (MVA2005), Tsukuba Science City, Japan, 16-
18 May 2005. Fleyeh implemented a recognition process
for identifying traffic signs using color variations and color
segmentation algorithms based on fuzzy sets. For
identifying the colors in the images, they used a set of
fuzzy rules. The output is shown in the form of a graph
that shows the possibility of a given input image. Here
European data sets are used.
[7] G.K. Siogkas, and E.S. Dermatas, “Detection, Tracking
and Classification of Road Signs in Adverse Conditions”,
MELECON 2006, pp. 537-540, May 2006. In this paper the
data sets used are from Portuguese roads and they are
classified by analyzing color information and then
classified according to their shape. Detection of circular
shape images is the main merit of the, but the demerit is
detecting the large size images. In future this algorithm
can be improved for critical illumination conditions.
[8] Sardar O. Ramadhan1, Burhan ERGEN 2, “Traffic Sign
Detection and Recognition”, International Journal of
Advanced Research in Electrical, Electronics and
Instrumentation Engineering, recognition of traffic sign,
Vol. 6, Issue 2, February 2017, pp.960. In this approach
the author prepared a computer-based system that can
detect traffic signs using a Blob analysis. For recognizing
traffic signs, the author concentrated on two main
categories they are extracting the attributes and
classifying the images. For this approach proper
illumination is very important. For reducing the number of
distinct colors quantization of colors is used. Coming to
the output a small daily box will appear along with the
predicted value of the given input image.
[9] S. Vitabile and F. Sorbello, "Pictogram Road signs
detection and understanding in outdoor scenes,"
presented at Conf. Enhanced and Synthetic Vision,
Orlando, Florida, 1998. In this paper the author opter
neural networks to recognize the traffic signs. Data set
used here is a Swedish traffic sign. For eliminating the
effects of shadows and illumination a new kind of color
detection algorithm is used. It converts RGB images into
HSV base for classifying them. The main advantage of this
approach is high robustness and correct segmentation. For
training the more correctly we can use a greater number
of images to get more accurate results.
3. METHODOLOGY PROPOSED
3.1 Deep Learning
A subset of machine learning that tells computers what to
do in any way like how humans do is called Deep learning.
It is the main technique behind many new technologies
like autonomous cars which make decisions while driving
without humans. In phones or tablets deep learning is
used for voice control and many other features. It is
mainly used for classification tasks. It has the capability to
classify or identify various images, texts or sounds.
Different models based on deep learning achieve higher
results and accuracy compared to humans. All these
models are trained by using large datasets. Nowadays
deep learning is used in many fields like Aerospace,
Electronics, Medical Research and We can see the future in
Deep learning.
3.2 Conventional Neural Network (CNN)
For classifying images Deep Learning uses an algorithm
called CNN which is an acronym for Conventional Neural
Network. Generally, it consists of input layer output layer
and many hidden layers. The main aim for having these
many hidden layers is to detect and learn different
features of input data at different layers of the network.
Convolution, activation and pooling are mostly layers used
for CNN. In convolution filters are added to input data for
highlighting features of the dataset. In the activation layer
only the data that meets our conditions is forwarded to the
next layer. And the pooling layer simplifies the output and
gives the results. After learning about features of input
data in all the layers, next it shifts to the classification
layer. The final layer in CNN is classification where it
classifies the input that came from pooling layers and
provides the final output.

Fig: CNN model
3.3 Keras
Many deep learning models developed so far use a library
called Kera. It is written in Python. Advantages of keras is
it is open-source and highly extensible and user friendly.
Major backend engines used for building deep learning
models are TensorFlow, Theano, CNTK. TensorFlow is
widely used for research and it can run on various CPUS
and GPUS and mobile os. Theano is used for various
mathematical calculations in multidimensional arrays.
Many multinational companies like Netflix, Uber, Square,
Yelp use Keras to develop many public domain products.
3.4 System architecture:
Our model mainly consists of 8 phases which are
displayed in the figure below. The first phase is a
collection of data sets which consists of various traffic
signs in various shapes and sizes. In the second phase we
train the model based on the above formed data set. After
that in the third phase we will test our model using a data
set called test. The most important phase in our model is
the fourth phase where we take input from the user and
predict the outcome based on the trained model and once
a sign is recognized the output is displayed on the screen.
Fig: System Architecture
4 WORKFLOW OF PROPOSED METHOD
The below image shows a series of tasks that must be
performed to complete the proposed model correctly.
Input
Convolution
Pooling
Classification
Output
D
a
t
a
s
e
t
One
hot
enco
ding
Cn
nn
m
od
el
Dat
a
set
trai
nin
g
U
s
e
r
I
n
p
u
t
i
m
a
g
e
Rec
ogni
tion
stag
e
Si
g
n
re
co
g
ni
ze
d
Results
of trained
data set
are used
for
recognizi
ng the
input
image

Fig : Project Workflow
5. RESULTS
After taking input from the image will be classified and the
extracted fractures will be compared with the images used
for training the model. And image to which maximum
features matches will be given as input and along with that
graph plotting accuracy and loss function are also
displayed.
Fig: Giving Input to the model
Fig: Plotting loss function.
Fig: Plotting Accuracy
6. CONCLUSION
For maintaining road safety, it is important to follow
traffic signs. For self-driving cars identifying road signs is
the most difficult task. So, the proposed system Detection
of road signs using CNN helps them to detect the unclear,
tampered and wrongly oriented traffic signs by detecting
the outliers and extracting the features of the given input
image. Using this model, we achieve 95% accuracy. We
perceived the changes in accuracy and loss function for

larger data sets. We can build this model using other
techniques-based color detection and feature extraction
techniques like SVM and SIFT. To make the model more
user friendly we can develop a Graphical User Interface to
input the image. Using GUI users can easily understand the
working of the model and how different signs are
classified.
7. REFERENCES
[1]Carlos Filipe Moura Paulo, “Detection and Recognition
of Traffic Signs” detection of traffic signs, pp. 4, sep.2007.
[2] Zhang, Z.J.; Li, W.Q.; Zhang, D.; Zhang, W. A review on
recognition of traffic signs. Proceedings of the 2014
International Conference on E-Commerce, E-Business
andE- Service (EEE), Hong Kong, China, 1–2 May 2014; pp.
139–144.
[3]Arunima Singh | Dr. Ashok Kumar Sahoo "Traffic Sign
Recognition" Published International Journal of Trend in
Scientific Research and Development (ijtsrd), ISSN: 2456-
6470, Volume-2 | Issue-4, June 2018, pp.122-126.
[4]Rubén Laguna*, Rubén Barrientos*, L. Felipe Blázquez*,
Luis J. Miguel**, “Traffic sign recognition application based
on image processing techniques”,Preprints of the 19th
World Congress the International Federation of Automatic
Control Cape Town, South Africa, background of study on
traffic signs, pp.104, Aug 24-29, 2014.
[5]Mahammad A. HANNAN1, Safat B. WALI*1, Tan J. PIN1,
Aini HUSSAIN1, Salina A. SAMAD1, “Traffic Sign
Classification based on Neural Network for Advanced
Driver Assistance System” classification of traffic signs,
pp.169, Nov 2014.
[6]Fleyeh, H., "Road and Traffic Sign Color Detection and
Segmentation - A Fuzzy Approach" Machine Vision
Applications (MVA2005), Tsukuba Science City, Japan, 16-
18 May, 2005.
[7]G.K. Siogkas, and E.S. Dermatas, “Detection, Tracking
and Classification of Road Signs in Adverse Conditions”,
MELECON 2006, pp. 537-540, May 2006.
[8]Sardar O. Ramadhan1, Burhan ERGEN 2, “Traffic Sign
Detection and Recognition”, International Journal of
Advanced Research in Electrical, Electronics and
Instrumentation Engineering, recognition of traffic sign,
Vol. 6, Issue 2, February 2017, pp.960.
[9] S. Vitabile and F. Sorbello, "Pictogram road signs
detection and understanding in outdoor scenes,"
presented at Conf. Enhanced and Synthetic Vision,
Orlando,
[10]M. Betke and N. Makris, "Fast Object recognition in
noisy images using simulated annealing," presented at
Fifth Inter. Conf. on Computer vision, Cambridge, MA, USA,
1995.
[11]M. Bénallal and J. Meunier, "Real-time color
segmentation of road signs," presented at Canadian Conf.
on Electrical and Computer Engineering (IEEE CCECE),
Montréal, Canada, 2003.
[12] D. Kang, N. Griswold, and N. Kehtarnavaz, "An
invariant traffic sign recognition system based on
sequential color processing and geometrical
transformation, "presented at IEEE Southwest Symposium
on Image Analysis and Interpretation, Dallas, Texas, USA,
1994.
[13]D. Kang, N. Griswold, and N. Kehtarnavaz, "An
invariant traffic sign recognition system based on
sequential color processing and geometrical
transformation," presented at IEEE Southwest Symposium
on Image Analysis and Interpretation, Dallas, Texas, USA,
1994.
[14] Jingwei Cao, Chuanxue song, Silun Peng, Feng xiao,
shixin song, “Improved Traffic Sign Detection And
Recognition Algorithm For Intelligent Vehicles”.
[15] Arunima Singh,Dr. Ashok Kumar Sahoo “Traffic Sign
Recognition”
[16] Ruben Laguna, Ruben Barrientos, L. Felipe Blazquez,
Luis J. Miguel ”Traffic sign recognition application based
on image processing techniques”.
[17] Carlos Filipe Moura Paulo ”Detection and Recognition
of Traffic Signs”.

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