An Analysis of Various Deep Learning Algorithms for Image Processing

VIVA-Tech International Journal for Research and Innovation Volume 1, Issue 2 (2019)
ISSN(Online): 2581-7280 Article No. 13
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www.viva-technology.org/New/IJRI
An Analysis of Various Deep Learning Algorithms for Image
Processing
Geeta S. Lagad1
, Ankit J. Maurya2
, Kunal D. Mestry3
, Dnyaneshwar Bhabad4
1,2,3,4
(Computer Engineering Department, VIVA Institute of Technology, India)
Abstract: Various applications of image processing has given it a wider scope when it comes to data analysis.
Various Machine Learning Algorithms provide a powerful environment for training modules effectively to
identify various entities of images and segment the same accordingly. Rather one can observe that though the
image classifiers like the Support Vector Machines (SVM) or Random Forest Algorithms do justice to the task,
deep learning algorithms like the Artificial Neural Networks (ANN) and its subordinates, the very well-known
and extremely powerful Algorithm Convolution Neural Networks (CNN) can provide a new dimension to the
image processing domain. It has way higher accuracy and computational power for classifying images further
and segregating their various entities as individual components of the image working region. Major focus will
be on the Region Convolution Neural Networks (R-CNN) algorithm and how well it provides the pixel-level
segmentation further using its better successors like the Fast-Faster and Mask R-CNN versions.
Keywords – Image processing, data analysis, machine learning, support vector machine, random forest
algorithms, deep learning, artificial neural networks, convolution neural networks, region convolution neural
networks
1. INTRODUCTION
This model itself will make use preconfigured weight matrices to identify the traffic-density in the scene and
thus differentiate it precisely. It will consider the different features of the images provided as input data in a
convolution. [4] The model itself will be capable of analyzing and identifying any kind of traffic scene as it
works on the Region Convolutional Neural Networks (R-CNN). [2] As the features aren’t predetermined as
probabilistic data to the system, it should be able to work with any random traffic scene which is the overall
motto behind using deep learning algorithms.
2. LITERATURE REVIEW
A. Khan, et. al. [1] have proposed a system is developed to control and monitor the congestion of traffic. The
principle inspiration is to distinguish the nearness and nonappearance of vehicles out and about utilizing factual
methodology coordinated with traditional picture preparing strategies. For this reason, they have build up a
"Probability Based Vehicle Detection (PBVD)" calculation based Vehicle Detection System (VDS) coordinated
with post - handling subsystems to frame a total traffic control framework. The framework has the ability to

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acquire vehicle insights amid controlling traffic. Reenactments are performed by creating total model traffic
engineering. Correlation is finished utilizing the outcome gained from model framework and preparing a
constant video of traffic scene. Reenactment results demonstrate the viability of the proposed plan.
Shreyas, et. al. [2] have proposed Automatic Number Plate Recognition (ANPR) System is based on an image
processing technology. The proposed framework can be fundamentally used to screen street traffic exercises, for
example, the distinguishing proof of vehicle amid petty criminal offenses, for example, speed of vehicle and to
identify at the road traffic signals path infringement. What's more, in this manner can be followed each vehicle
for traffic rule infringement and can give the data to the worry expert to make further successful move. The
proposed framework initially identifies for any vehicle which abuses traffic principle and afterward catches the
vehicle picture. From the caught picture utilizing picture division procedure the vehicle number plate district
will be extricated. Furthermore, the system utilized for the character acknowledgment on number plate is
Optical character acknowledgment. The framework is executed and reproduced utilizing MATLAB.
Z. Shao, et. al. [3] have proposed in this paper the recognition framework of car makes and models from a single
image captured by a traffic camera. Due to various configurations of traffic cameras, a traffic image may be
captured in different viewpoints and lighting conditions, and the image quality varies in resolution and color
depth. In the framework, cars are first detected using a part-based detector, and license plates and headlamps are
detected as cardinal anchor points to rectify projective distortion. Car features are extracted, normalized, and
classified using an ensemble of neural-network classifiers. In the experiment, the performance of the proposed
method is evaluated on a data set of practical traffic images. The results prove the effectiveness of the proposed
method in vehicle detection and model recognition.
K. Sohn, et. al. [4] have proposed existing methodologies to count vehicles from a road image have depended
upon both hand-crafted feature engineering and rule-based algorithms. These require many predefined
thresholds to detect and track vehicles. This paper provides a supervised learning methodology that requires no
such feature engineering. A profound convolutional neural system was conceived to check the quantity of
vehicles on a street fragment dependent on video pictures. The present strategy does not view an individual
vehicle as an article to be distinguished independently; rather, it all in all checks the quantity of vehicles as a
human would. The test outcomes demonstrate that the proposed procedure beats existing plans. For the most
part, it is hard to represent how a CNN can check the quantity of vehicles precisely. In any case, channels were
relied upon to extract the highlights of a picture, objects were perceived by the highlights, and the articles were
then tallied by means of the last completely associated layer.
Deepak, et. al. [5] have proposed Recently Deep Learning has shown remarkable promise in solving many
computer vision tasks such as object recognition, detection, and tracking. Nonetheless, preparing profound
learning structures require tremendous named datasets which are tedious and costly to gain. In this paper they
evade this issue by information enlargement. By appropriately enlarging a current extensive general (non-
traffic) dataset with a little low-goals heterogeneous traffic dataset (that we gathered), we get best in class
vehicle identification execution. As far as we could possibly know the gathered dataset, named IITM-HeTra, is
the first openly accessible marked dataset of heterogeneous traffic.

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Gu, et. al. [6] have proposed a method for real-time vehicle detection and tracking using deep neural networks is
proposed in this paper and a complete network architecture is presented. Using these model, you can obtain
vehicle candidates, vehicle probabilities, and their coordinates in real-time. The proposed model is trained on
the PASCAL VOC 2007 and 2012 image set and tested on ImageNet dataset. By a carefully design, the
detection speed of these model is fast enough to process streaming video. Experimental results show that
proposed model is a real-time, accurate vehicle detector, making it ideal for computer vision application. This
network includes 9 convolutional layers, 4 inception modules, one SPP layer and 2 fully connected layers.
Limitations of these network architecture is these system struggles with small and nearby object in groups.
P. Bajaj, et. al. [7] have proposed vehicle detection and vehicle classification using neural network (NN), can be
achieved by video monitoring systems. In most vehicle detection methods in the literature, only the detection of
vehicles in frames of the given video is emphasized. However, further analysis is needed in order to obtain the
useful information for traffic management such as real time traffic density and number of vehicle types passing
through roads. This paper presents application of neural network for vehicle detection and classification. In
scenes where the density of traffic is very high, causing many vehicles to occlude each other, the algorithms
could detect multiple vehicles as a single vehicle, thus affecting the count and also causing a misclassification.
These paper is useful for our system to detect individual vehicle.
R.Girshick, et. al. [8] have proposed the method, called Mask R-CNN, expands Faster R-CNN by including a
branch for anticipating an article veil in parallel with the current branch for bouncing box recognition.In
guideline Mask R-CNN is an instinctive expansion of Faster R-CNN. Veil R-CNN accomplishes great outcomes
even under testing conditions. Despite the fact that Mask R-CNN is quick, proposed configuration isn't
enhanced for speed, and better speed/precision exchange offs could be accomplished. Division is a pixel-to-
pixel assignment and endeavor the spatial format of covers by utilizing a FCN. Our methodology productively
recognizes questions in a picture while all the while creating an amazing division cover for each example.
Bishop, et. al. [9] have a proposed system has adopted two different algorithms that are built upon on region-
based object detection, which achieves state-of-the-art performance on object detection tasks from other
common object detection datasets such as PASCAL VOC and ImageNet. They have applied two region-based
models to the Nvidia AI City dataset, evaluated their performance under different training settings and achieved
state-of-the-art performance on the dataset. The frameworks used in the proposed system, while achieving a
competitive result, still have much room for improvement.
Goodfellow, et. al. [10] have proposed, In this paper, an efficient license plate recognition system had proposed
that first detects vehicles and then retrieves license plates from vehicles to reduce false positives on plate
detection. Then, apply convolution neural networks to improve the character recognition of blurred and obscure
images. The results show the superiority of the performance in both accuracy and performance in comparison
with traditional license plate recognition systems. The proposed LPRCNN model is composed of two
convolutional layers, two maxpooling layers, two fully connected layers, and one output layer.

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Y. Lin, et. al. [11] have a Proposed system used convolutional neural network on Keras with Tensorflow support
the experimental results shows the time required to train, test and create the model in limited computing system.
The system is trained with 60,000 images with 25 epochs each epoch is taking 722 to 760 seconds in training
step on Tensorflow cpu system. At the end of 25 epochs the training accuracy is 96 percentage and the system
can recognition input images based on train model and the output is respective label of images. We chose to
utilize 60,000 pictures with a 32x32 pixel measure CIFAR-10 database. Python and TensorFlow has been
utilized for the program. They chose to utilize 60,000 pictures with a 32x32 pixel measure CIFAR-10 database.
Akhil, et. al. [12] have proposed a reformative CNN structure is presented to enhance recognition accuracy and
greatly reduces the computational cost incurred when running a CNN. By introducing transfer learning for our
system, accuracy rate is significantly improved. Extensive experiments have been performed, yielding
promising results. In addition, a reformative fine tuned CNN structure is presented to enhance recognition
accuracy. CNN was previously trained using the CIFAR-10 data set, which has 50,000 training images. So this
pre-trained CNN is tuned for vehicle using only 40on road vehicle images.
3. ANALYSIS TABLE
Sr.
No.
Title Of Paper Technique DataSet Used Accuracy/
Efficiency
1
Modeling, Design and
Analysis of
Intelligentm Traffic Control
System
Based on Integrated Statistical
Image Processing
Techniques.[1]
.Probability Based
Vehicle
Detection(PBVD)
algorithm
A. Vehicle Detection
System (VDS)
B. Vehicle Counting
and
Classification System.
(VCCS)
The final results
are
satisfactory and
show
that the system
can cope with a
noisy
environment.
2
Dynamic Traffic Rule
Violation
Monitoring System Using
Automatic
Number Plate Recognition
with SMS
Feedback.[2]
Automatic Number
Plate Recognition
(ANPR) System.
-
We are able to
achieve
95% of success
rate in
number plate
detection.
3
Recognition of Car Makes and
Models From a Single Traffic-
Camera
Image.[3]
Naive Bayes
SVM Classifier
Cascade Classifier.
Practical Traffic
Images.
To improve the
recognition
precision of car
models above
60% accuracy.
4
Image-Based Learning to
Measure Traffic
Density Using a Deep
Convolutional
Neural Network.[4]
Convolutional
Neural Networks.
(CNN)
Snapshots from video
streaming.
Acceptable
Accuracy.

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5
Training a Deep Learning
Architecture for
Vehicle Detection Using
Limited
Heterogeneous Traffic Data.[5]
Hybrid
computational
intelligent
techniques and fuzzy
neural
networks is applied
to control the traffic
signals.
IITM-HeTra.
High degree of
accuracy.
6
Real-time vehicle detection
and tracking
using deep Neural networks.[6]
Convolutional
Neural Networks.
(CNN)
ImageNet Dataset.
80.5 % in
detecting
vehicles.
7
Vehicle Detection and Neural
Network
Application for Vehicle
Classification.[7]
Fuzzy neural
networks.
- 90%
8
Vehicle Classification using
Neural Networks.[8]
Radial Basis
Function Networks.
(RBFN)
ImageNet Dataset.
71 % in
detecting
vehicles.
9 Mask R-CNN.[9] - -
Pixel-to-pixel
alignment.
10
Effective Object Detection
From Traffic
Camera Videos.[10]
Faster RCNN and
Region-based
Fully Convolutional
Network.
ImageNet.
10−40% higher
mean
average precision
(mAP)
compared with
other
solutions.
11
An Efficient License Plate
Recognition
System Using Convolution
Neural
Networks.[11]
LPR convolution
neural networks.
-
99.2% of
character
recognition
accuracy.
12
Moving Vehicle Detection
Using Deep
Neural Network.[12]
Recurrent
Convolution Neural
Networks.(RCNN)
CIFAR-10
100% accuracy
with
respect to
detection
accuracy.
4. CONCLUSION
The above paper mainly discussed deep learning techniques like RCNN, RNN, and CNN which gives different
results on different datasets giving varied accuracy. Large number of datasets were used for proper prediction.
The research shows text sequence prediction can be implemented through deep learning techniques which can
change the scenario of typing whole sentences.
The study of various papers gives a clear edge stating deep learning might provide better results when compared
with other techniques. Previously, machine learning and natural language processing were used in prediction but
deep learning models produced better accuracy.

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Proceedings of the Twenty-Ninth AAAI Conference on AI 2015.
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Conference on ICT and Knowledge Engineering (ICT&KE)
[3] W. Yin, K. Kann, Mo Yu and H. Schütze, “Comparative study of CNN and RNN for Natural Language
Processing”, Feb-17.
[4] Z.Shi, M. Shi and C. Li, “The prediction of character based on Recurrent Neural network language model”,
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[7] J. Lee and F. Dernoncourt, “Sequential Short-Text Classification with Recurrent and Convolutional Neural
Networks”, Conference paper at NAACL 2016.
[8] M. Liang and X. Hu, “Recurrent Convolutional Neural Network for Object Recognition”, 2015 IEEE
Conference on Computer Vision and Pattern Recognition (CVPR).
[9] A. Hassan and A.Mahmood, “Deep Learning for Sentence Classification”, 2017 IEEE Long Island Systems,
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[11]W. Yin and H. Schutze, “Multichannel Variable-Size Convolution for Sentence Classification”, 19th
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[12] I.Sutskever, O.Vinyals and Q. V. Le, “Sequence to Sequence Learning with Neural Networks”, Dec-14.
[13]Y. Zhang, B. Wallace, “A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks
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and Clustering” World Symposium on Digital Intelligence for Systems and Machines (DISA), 2018
[15]Y. Lin, J. Wang, “Research on text classification based on SVM-KNN” IEEE 5th International Conference on
Software Engineering and Service Science, 2014
[16]A. Hassan, A. Mahmood, “Convolutional Recurrent Deep Learning Model for Sentence Classification”, IEEE
Access, 2018

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