VEHICLE CLASSIFICATION USING THE CONVOLUTION NEURAL NETWORK APPROACH

Scientific Journal of Silesian University of Technology. Series Transport
Zeszyty Naukowe Politechniki Śląskiej. Seria Transport
Volume 112 2021
p-ISSN: 0209-3324
e-ISSN: 2450-1549
DOI: https://doi.org/10.20858/sjsutst.2021.112.16
Journal homepage: http://sjsutst.polsl.pl
Article citation information:
Trivedi, J., Devi, M.S., Dhara, D. Vehicle classification using the convolution neural network
approach. Scientific Journal of Silesian University of Technology. Series Transport. 2021,
112, 201-209. ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2021.112.7.16
Janak TRIVEDI1, Mandalapu Sarada DEVI2, Dave DHARA3
VEHICLE CLASSIFICATION USING THE CONVOLUTION NEURAL
NETWORK APPROACH
Summary. We present vehicle detection classification using the Convolution
Neural Network (CNN) of the deep learning approach. The automatic vehicle
classification for traffic surveillance video systems is challenging for the Intelligent
Transportation System (ITS) to build a smart city. In this article, three different
vehicles: bike, car and truck classification are considered for around 3,000 bikes,
6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract
different vehicle dataset’s different features without a manual selection of features.
The accuracy of CNN is measured in terms of the confidence values of the detected
object. The highest confidence value is about 0.99 in the case of the bike category
vehicle classification. The automatic vehicle classification supports building an
electronic toll collection system and identifying emergency vehicles in the traffic.
Keywords: convolution neural network, vehicle classification, deep learning,
intelligent transportation system
1
Faculty of Electronics & Communication Engineering Department, Gujarat Technological University,
Government Engineering College Bhavnagar-364002, Gujarat, India. Email: Trivedi_janak2611@yahoo.com.
ORCID: 0000-0002-8662-5153
2
Principal, Ahmedabad Institute of Technolog-380060, Gujarat Technological University, Gujarat, India.
Email: saradadevim1@gmail.com. ORCID: 0000-0003-4904-1906
3
Faculty of Electronics & Communication Engineering Department, Gujarat Technological University,
Government Engineering College Bhavnagar-364002, Gujarat, India. Email: dave.dhara24888@gmail.com.
ORCID: 0000-0002-7724-800X

202 J. Trivedi, M.S. Devi, D. Dhara
1. INTRODUCTION
Vehicle classification, vehicle speed measurements, electronics toll connection system,
vehicle counting, and vehicle recognition system are numerous ITS applications. The vehicle
classification system helps to build an electronic toll collection system and vehicle recognition
system in a smart city. Automatic vehicle classification plays a central role in developing an
intelligent system traffic management system. The automatic extraction of a vehicle from a
surveillance video is a hot research topic for a smart city's traffic organisation.
The practice of CCTV camera systems helps in managing traffic control in metropolitan
areas. The vehicle classification can be done in two ways: using the human operator and the
computer-vision system. Computer vision-based traffic management is more innovative and
efficient compared to human operators.
Deep learning and machine learning revealed a new direction with high accuracy for an
automatic vehicle recognition system in the field of computer-vision techniques. The well-
known Convolution Neural Network (CNN) application, which is part of deep learning,
automatically helps classify vehicles using image processing techniques. Deep learning works
similarly like the human brain for the proposed system [9]. The layer-by-layer structure in deep
learning extracts valuable information for the given dataset.
1.1. Motivation
The image processing techniques manage traffic control systems without prior installation
of different sensors on the road. Vehicle classification is still a challenge due to meagre
processing time for real-time applications, different resolution of images, illumination changes
in the video, and other disturbances. There is main problems in vehicle classification using the
traditional method – object's classification requires human supervision. The images are location
variants for the defined method.
CNN can overcome these limitations for object classification. The recent development in the
area of CNN boosts the research direction in vehicle detection, identification, and classification.
Vehicle classification is applicable in numerous applications, including robotics, automatic toll
collection system, and recognises the emergency vehicle. The classification of a vehicle can be
done online, offline, and in a real-time application.
The remaining part of this paper is organised as follows: Section two reports the literature
survey. Section three presents CNN using the deep learning approach with different layer’s
explanations. Next, section four is devoted to experimental results. Finally, section five presents
the discussion and conclusion and the future scope is drawn in section six.
2. LITERATURE SURVEY
The recent advancement of the deep learning approach using the CNN structure simplifies
the use of ITS in traffic management. The traffic surveillance video with low-resolution images
suffers low accuracy in its output results. The results are varied for daytime and nighttime,
controlled environment conditions and uncontrolled environmental conditions. The Support
Vector Machine (SVM) and Artificial Neural Network (ANN) both have registered low
accuracy for the vehicle classification approach, according to Bautista et al. [3]. CNN has a
high accuracy rate for vehicle recognition compared to SVM and ANN. Bautista et al. [3]
explained CNN-based vehicle detection and classification using high-resolution images.

Vehicle classification using the convolution neural network approach 203.
Huseyin et al. [5] discussed detecting, tracking, and classification of multiple moving objects
in real-time. The image registration is used to align the two images that determine the key point
of the image. For increasing the speed of operation, greyscale images are used in the image
registration steps. A similar kind of operation is used in this article before resizing the original
images. The moving object detection using the frame differencing method between two
consecutive frames creates a problem with the image registration process. However, this can be
solved using different filtering operations, like median filtering and mean filtering. The
vehicles' classification has been demonstrated with the help of a deep learning-based CNN
approach and a threshold value. The image was first resized to 227 x 227 to match the network
layer.
The deep neural network approach-based vehicle detection is explained by Jun et al. [6]. The
two light-weighted CNN structures, one to propose Region of Interest (ROI) and the other to
make classification, are used. The input image size is 32 x 32 to light the CNN structure. Tang
et al. [10] demonstrated vehicle detection using state of art method like YOLOv2.
Bing et al. [2] discussed object detection for the surveillance video using the CNN structure
used in traceability applications. The system works in two-phase: target classification and target
location. Zhou et al. [12] demonstrated multiple vehicle detection using a combination of two
neural networks. One network detects vehicles with their type, and the other network predicts
vehicle speed with the vehicle location in a real-time condition.
Suhao et al. [7] outlined three types of vehicle detection and classification using the deep
learning method CNN. Ajeet et al. [1] highlighted different image datasets used in object
detection and classification. The comparison of deep learning-based object detection methods
with their features is explained. The different deep learning frameworks with their interface,
features, and deep learning model are discussed with the developer and licence.
Zhao et al. [13] considered the vehicle detection method using CNN of the deep learning
approach and multi-layer feature fusion. This method effectively detects vehicles and matches
the real-time level speed, which helps in smart driving cars. Vehicle detection in a traffic-
congested scene is not enough using this method, so it requires further study to improve this
method. The input image size in this CNN structure is 312 x 312. Wenming et al. [11] developed
a fast deep neural network for real-time video object detection; identify the ROI which contains
a target vehicle with high confidence.
3. METHOD DESCRIPTION
In this article, three different vehicle classification (bike, car, and truck) and recognition was
done using a deep learning-based CNN approach. The key procedure for vehicle classification
using CNN can be summarised into four steps: define each image to network, prepare a layer,
train, and create a network, test network with accuracy. In the proposed method, we involved
only images of bikes, cars, and trucks. To develop a network using deep learning, we have
collected around 3,000 images of bikes, 6,000 images of cars, and 2,000 images of trucks. Thus,
a total of 11,000 images were used to classify and recognise three different categories of objects.
The different images have been simulated to verify the proposed method. The training of a
network depends on the network speed and processing power.

3.1. Convolution Neural Network
CNN is a type of neural network that is usually applied to image processing problems. The
different CNN applications including identified any object in an image, driverless car, robotic.
They are helpful for fast-growing areas, and that is also one of the main important factors for
deep learning and artificial intelligence today. CNN is unique and different from the simple
Neural Networks (NN). It is effective for processing and classifying images.
A simple NN has an input layer, a hidden layer, and an output layer. The input layers accept
input in different forms, while the hidden layers perform calculations on these inputs. The
output layer then delivers the outcome of the calculations and extractions. Each of these layers
contains neurons connected to neurons in the previous layer. Each neuron has its weight, that
is, no assumption is made about the data being fed into the network. This approach is not
suitable for images or language.
CNN works treat data as spatial. Instead of neurons being connected to every neuron in the
previous layer, they are only connected to the neurons close to it, and all have the same weight.
The simplification in the connections means the network upholds the spatial aspects of the data
set. The word 'Convolutional' refers to the filtering process that happens in this type of network.
If an image is complex, like too many objects in one image, and thus challenging to identify the
object, CNN can simplify it. Therefore, it can be better processed and understood. Like the
simple NN, CNN is made up of multiple layers. There are different layers of CNN, which makes
it unique. This is explained in section 3.2.
The flowchart of the proposed method is shown in Figure 1. Every neural network has a
series of layers; the more layers it has, the more profound the network. Each layer takes in data
from the previous layers, transforms the data, and then processes it. Thus, the first layer takes
in the raw input image, and by the time it gets to the last layer, it will likely pick out the correct
name of the vehicle in the original image.
3.2. Different layers
Here, the described layers learn and train using the Matlab software, and detail help is
given in https://www.mathworks.com/ [8].
(1) Input image layer: The input image layer specifies input image size properties. In this article,
28x28x1 (height, width, and a number of a channel) image size is used. The zero centre
normalisation, which subtracts the mean, is used.
(2) Convolution layer: This layer is the most important. It works by placing a filter over an
array of image pixels. Convolution layer with 3x3 filter size with stride [1 1] and padding [1 1
1 1]. Stride represents the filter's movements over the images, and padding '1' means several
pixels added to the CNN. This process creates a convolved feature map.
(3) Relu layer: Relu layers acts as an activation function. A Rectified Liner Unit (Relu) carries
out threshold operation to the input image element. When any value is less than zero, then those
values are set to zero themselves.
(4) Maxpool layer: The pooling layer down samples or reduces the sample size. In this article,
a maxpool layer with stride [2 2] and padding [0 0 0 0] is used, which makes processing much
faster as it reduces the number of parameters the network needs to process.

(5) Fully connected layer: The fully connected layers allow us to perform classification on the
dataset. A fully connected layer takes values from the relu layer process by multiplying the
input by weight of matrix and then adding a bias vector. The fully connected layer with output
size three is used for the classification of three different objects.
(6) Softmax layer: The softmax layer applies a softmax function to the input. The softmax
transformation of a linear function was explained by C. M. Bishop [4] as shown in equation 1,
where y (x) is a softmax function, a (x) is a linear function of x.
1
exp( ( ))
( )
exp( ( ))
r
r k
j
j
a x
y x
a x



,
0 ( ) 1
r
y x
 
, and 1
1
k
j
j
y


 (1)
Softmax is applied after the final fully connected layer and before the last classification layer.
(7) Classification layer: The classification layer is the last layer for multi-class classification
problems. The last layer in the CNN indicates the number of classes from the previous layer
computation. The classification layer follows the softmax layer. Classification layers use
equation 2 from C. M. Bishop [4] book as mentioned below,
1 1
ln(y )
N k
ij ij
i j
Loss t
 
 
,
where K and N indicate the number of classes and samples, respectively.
(2)
The first step is to separate and store each collected image with different labels in the system.
The stored images have different sizes, so the next step is to resize all the images into 28 x 28
image size and convert all colour images into grey images. Thereafter, save this database for
further processing.
As shown in Figure 1, twelve different layers are used to perform CNN for vehicle
classification for the collected data and classify it into three different objects: bike, car, and
truck. The twelve different layers include three convolution layers and relu layers, two maxpool
layers, one image-input layer, a fully connected layer, a softmax layer, and finally, a
classification layer after completing the CNN layers for classification of the different vehicles,
next trained the network and test the network using different images for the three different
vehicles.
4. EXPERIMENTAIL RESULT
The result section indicates recognition of the image and accuracy in terms of confidence
values concerning the other two objects. The confidence values indicate the matching
percentage to the respective objects. In Table 1, the highest confidence value of the bike image
is about 99%, and the Figure with a recognition graph is shown in Figure 3a,b). The car-type
vehicle is shown in Figure 2a, and its confidence value to the car, bike, and truck is shown in
Figure 2b. The car-type vehicle's confidence value is above 85%, as shown in Figure 2b.
The truck-type vehicle is shown in Figure 4a, and its confidence value to the car, bike, and truck
is shown in Figure 4b.

Fig. 1. Vehicle classification using CNN
Table 1 represents different images of bikes, and they recognise with higher accuracy
compared to cars and trucks.
Tab. 1.
Images of bikes and they recognise with
higher accuracy compared to cars and trucks
Images Bike Car Truck
1 0.88 0.1 0.02
2 0.92 0.07 0.01
3 0.86 0.13 0.01
4 0.84 0.12 0.04
5 0.87 0.12 0.01
6 0.99 0.01 0
7 0.93 0.06 0.01
8 0.92 0.07 0.01
9 0.86 0.1 0.04
10 0.92 0.06 0.02

5. DISCUSSION
The three types of vehicles: bike, car, and truck, classify and recognise using the deep
learning-based CNN approach. CNN has a specific advantage over traditional vehicle
classification methods. In CNN, the different layers share the relevant information, which
invariance to a scalar, rotation, geometric transformation, and illumination changes. This
approach can identify a fault and re-identify the object with another training of the network.
The accuracy of the CNN can be improved with many image datasets with different conditions
of images.
Fig. 2. (a) The car, (b) The car recognises with higher accuracy compared to truck and bike
Fig. 3. (a) The bike, (b) The bike recognises with higher accuracy compared to car and truck
It first categorised data into bike, car, and truck using a deep learning approach. The image
dataset then resizes to 680 x 480 pixels. The larger and lower size of the image can affect the
validation process. The higher image dataset's size requires a long computational time
compared to the image dataset's smaller size. The image size should be such that it requires low
computational time as well as high recognition accuracy. Afterwards, apply different layers of
convolution neural layer to the desired image datasets.

Fig. 4. (a) The truck, (b) The truck recognises with higher accuracy compared to bike and car.
In this article, about 11,000 images for the three types of vehicles: bike, car, and truck are
trained using CNN. The highest confidence value is found in the case of a bike. The simulation
result of the CNN approach is summarised in Table 1. From around 3,500 images of bikes,
simulated 10 bikes images, the result is verified using the confidence matrix concerning the car
and truck. Image no. six in Table 1 has a confidence value of 0.99 for bike prediction, 0.01 for
car prediction, and 0.0 for truck prediction. This indicates the accuracy of simulation for bike
prediction near 99%.
Similarly, from around 5,800 images of cars, we randomly checked the simulation result of
'car.' We identified 'car' as 'car' with the highest confidence values around 85%. From around
1,800 images of trucks, we identified 'truck' as 'truck' with the highest confidence values around
'55%'.
6. CONCLUSION
In this paper, we presented a fast and robust vehicle classification using a deep learning-
based CNN approach. This approach orders vehicles into three categories: bike, car, and truck.
The accuracy of the classified object is measured using confidence values for selected images.
The deep learning approach using CNN is the backbone for vehicle classification. The manual
labelling for vehicle identification is time-consuming, laborious work that can be avoided using
deep learning CNN.
Accordingly, a higher amount of efficiency is required to accept ITS in a traditional traffic
management system. The bike is recognised accurately with the current image datasets. The car
is recognised with higher accuracy than the truck but with lower accuracy than the bike. The
least accuracy we received is the case of the truck. The truck recognisation result is low due to
the tested dataset containing a meagre amount of truck images compared to bike and car. We
will try to overcome this limitation in our future work. CNN is less accurate for a smaller data
training set but presents higher accuracy with an extensive image dataset.

References
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Received 18.03.2021; accepted in revised form 19.06.2021
Scientific Journal of Silesian University of Technology. Series Transport is licensed under
a Creative Commons Attribution 4.0 International License

VEHICLE CLASSIFICATION USING THE CONVOLUTION NEURAL NETWORK APPROACH

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