LANE DETECTION USING IMAGE PROCESSING IN PYTHON

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 04 | Apr 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 840
LANE DETECTION USING IMAGE PROCESSING IN PYTHON
Nidhi Lakhani1, Ritika Karande2, Deep Manek3, Vivek Ramakrishnan4
1-3BE Student, Electronics and Telecommunication, Atharva College of Engineering, Mumbai, India
4Professor, Electronics and Telecommunication, Atharva College of Engineering, Mumbai, India
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Abstract - When we drive, we use our eyes to decide where
to go. The lines on the road portray us where the lanes act as
our steady reference for where to steer the vehicle. Naturally,
one of the first things we would like to develop a self-driving
car is to detect lane lines using an algorithm automatically.
Lane detection is a difficult problem to solve. For decades, it
has piqued the interest of the computer vision community.
Lane detection is essentiallyamulti-featuredetectionproblem
that has proven to be difficult for computer vision and
machine learning algorithms to solve. We present an Image
Processing based method that involves Region Masking and
Canny Edge Detection. The prime goal is to use the cannyedge
detection algorithm to extract the features and then use
another method to detect the lanes on the region masked
image. As the images are not perfect every time, looping
through the pixels in order to find the slope and intercept
would be a difficult task. This is where we looked at the Hough
transform concept. It aids us in identifying prominent lines
and connecting disjoint edge points in an image.
Key Words: Lane Detection, Image Processing, OpenCV,
Hough Transform, Canny Edge Detection.
I. INTRODUCTION
Automobiles have become oneofthetransportationtoolsfor
people to travel due to the rapid development of society.
There are an increasing number of vehicles of various types
on the narrow road. Lane detection is a prominent topic in
the field of machine learning and computer vision and it has
been utilized in intelligent vehicle systems [1]. It is an
emerging field and finds its applications in the commercial
world. The lane detection system comes from the lane
markers in an intricate environment and is used to
approximate the vehicle’s position and trajectory relative to
the lane reliably [2]. At the same time, lane detection plays a
substantial part in the lane departure warning system. The
task of lane detection is mainly split into two parts: edge
detection and line detection. Line detection is equally
important as edge detection in the process of lane detection.
The most popular lane line detectors are the Hough
transform and convolution-basedtechniques.Lanedetection
is the course of detecting lane markers on the road and
thereafter presenting these locations to an intelligent
system. Intelligentvehiclescooperate withtheinfrastructure
to achieve a safer environment and better traffic conditions
in intelligent transportation systems.Theutilizationofa lane
detecting system could be as simple as pointing out lane
locations to the driver on an external display to more
intricate tasks such as predicting a lane change in an
instantaneous moment to avoidcollisionwithothervehicles.
II. GENERAL DESCRIPTION
In this project we have detected lane lines in images using
Python as it is one of the widely used programming
languages for this purpose and OpenCV. OpenCV stands for
"Open-Source Computer Vision", which is a module that has
numerous useful tools for analysis of images [3]. OpenCV
supports a wide variety of programming languages like
Python, and Java. It can assess images and videos to
recognize objects, faces, or even thehandwritingofa human.
In this project, we also use NumPy for a few simpler image
processing techniques.
The lane detection module is divided into two steps:
(1) Gaussian Smoothing and Canny Edge Detection and
(2) ROI selection and detecting lane lines using Hough
Transform.
Figure-1: Block Diagram
II.A. GAUSSIAN SMOOTHING
Gaussian blur, in image processing, is also known as
Gaussian smoothing. It is the outcome of blurring an image
by a Gaussian function [4]. Noise reduction is gained
by blurring the image with the support of a smoothing filter.
Gaussian Smoothing is a widely used effect in graphics
software, typically to reduce image noise. The 'kernel' for
smoothing actually expresses the shape of the function that
is used to compute the average of the neighboring points. A
Gaussian kernel is a kernel whichhastheshapeofa Gaussian
i.e., a normal distribution curve. In order to use the Gaussian
filter, we will first convert the image to grayscale so that it is
easier for us to distinguish the output we get after running
canny edge detector.

II.B. CANNY EDGE DETECTION
Edge detection is an image processing technique used to
recognize points in a digital image with discontinuities,
basically, sharp changes in the image brightness. These
points where the image brightness varies sharply are called
the edges (or boundaries) of the image. Canny edge [5]
detector is probably the most commonly used and most
effective method, because it uses first a gaussian filter, it has
more noise immunity than other methods. There is a
multitude of information available in the documentation of
OpenCV itself. Thus, we decided touseCannyEdgeDetection
for this project.
To summarize, first, the algorithm will detect strong edge
(strong gradient) pixels above the high threshold and reject
pixels below the low threshold. Next, pixels with values
between the low threshold and high threshold will be
included as long as they are connected to strong edges. The
output edges are a binary image with white pixels tracing
out the detected edges and black everywhere else. We will
also include Gaussian smoothing before running Canny,
which is basically a way of suppressing noise and spurious
gradients by averaging. cv2.Canny() function by OpenCV
actually applies Gaussian smoothing internally, but we
include it explicitly here becausewecangeta differentresult
by using further smoothing and it's not a changeable
parameter within cv2.Canny().
II.C. REGION MASKING
In an image, a region is a group of connected pixels thathave
similar properties. Because theymaycorrelatetoa itemsina
scene, regions are significant for image interpretation.
Region Masking [6] is the process that is underneath many
types of image processing, including edge detection, motion
detection, and noise reduction. Masking a region is a view-
specific graphic that can be used to hide certain and show
certain elements in a view. It is a non-destructive process of
editing images. Assuming that the camera (which is front-
facing) that captures the image is mounted in a fixed spot on
the car in a way that the lane lines will always appear in the
same general section of the image [7]. Here, we use a
triangular mask to illustrate the simplest case, but we can
use a quadrilateral, and in principle, we could use any
polygon. We would now use edge detection algorithm for
detecting lane lines rather than relying on color. This would
be a more reliable and efficient model.
II.D. HOUGH TRANSFORM
Now, to select lane lines in the edge detected image, we use
the Hough Transform. The Hough Transform is a transform
used to detect straight lines [8].Asperthedocumentation,to
apply the transform, it is first desirable to have an edge
detection pre-processing. In essence,a linecanbe spotted by
finding the number of intersections between curves [9]. The
more curves intersecting means that the line representedby
that intersection have more points. In general, we can
describe a threshold of the minimum number of
intersections needed to detect a line. This is what theHough
Transform does. It keeps track of the intersection between
curves of every point in the image. If the number of
intersections is over a certain threshold,thenitdeclaresitas
a line.
II.E. LANE DETECTION PIPELINE
The usual lane line detection method is to take an image of
the road first with the aid of a camera fixed in the vehicle.
Then the image taken is converted to a grayscale image to
minimize the processing time. Secondly, the appearance of
noise in the image will prevent the detection of the correct
edge. Hence, the filters should be applied to remove noises
like Gaussian filter. Then, the edgedetectorproducesanedge
image by using a canny filter with automatic thresholding to
obtain the edges. Next, the edged image would be sent to the
line detector after detecting the edges. Further, the Hough
Transform is used to detect the required polygon on an edge
detected image. Once the image goes through all these
intermediate steps, a pipeline is made using these steps as
functions with a single argument which is the source image.
Once the testing and validation parts on images are over, we
can move ahead with applying the same concept on lane
videos. Here, each frame of the video is treated as an image
and it goes through the lane detection pipeline. This step is
repeated for all the frames in the videos and later all these
frames are merged to create a single video file with the lane
detected output.
Now that we have established the techniques and functions
we need to use, we have created a lane detection pipeline.
Through this function, our input image will go through all
the required steps mentioned above.
II.F. IMPLEMENTATION
We will use Jupyter Notebook for code building purposeand
to input the video and image file. Then, we will show the
output file as a small GUI (Graphical User Interface)
application written in Python. For this purpose, we use
tkinter and moviepy library of Python.
III. RESULTS AND OUTPUT
Here, Figure 1 shows the output as a lane detected image on
our test images and Figure 2 shows the output when our
code was run on the test video.

Figure-2: Output images
Figure-3: Video output screenshot
IV. APPLICATIONS
It can be used for lane detection in a driverless car that isn’t
fully automated so that it can assist the driver.
V. ADVANTAGES
 It assists vehicular drivers as well as autonomous
cars to drive safely.
 This does not involve complex and advanced
machine learning, deep learning or neural
networks. Rather, relies on easy-to-understandand
simple image processing and computer vision
techniques.
 It has minimal cost of development as it uses open
source technologies such as Python, Jupyter and
OpenCV.
VI. DISADVANTAGES
 Uniformity of the markings present on the lanes is
an important factor in order to minimize confusion
and uncertainty. Our model needs lanes where
there are uniform and visible markings.
 The methods that have been developed so far work
efficiently and give good results in cases where
noise is not present in the image. But the problem
arises when lot of noise is in the road images, as it
does not give efficient results.
VII. CONCLUSION
With all the developments in the autonomous vehicle
industry, lane detection systems are going to be more in
demand. Through this project, we effectively tried to
propose the lane detection codewithouthavingtowritelong
codes that use machine learning or deep learning. Through
the Hough transform, the robustness and adaptability of the
detection results are enhanced. There is some need for good
dataset for rural road images which will broaden the
application area of our project. The presence of noise is just
one of the drawbacks but it can be rectified using good noise
minimizing algorithms. We have not used object detection
and tracking but it can be used to further incorporate more
features into the algorithm.
VIII. FUTURE SCOPE
To further improve the robustness of the algorithm, some
other methods can be considered in the future, such as, lane
detection can be implemented using guided image filtering
technique, for example, Gabor filter. Another issue to be
dealt with is fog removal. In the near future, one can modify
the existing Hough Transformation to measure both the
curved and straight roads. Various steps should be taken to
improve the results in different environmental conditions
like sunny, foggy, rainy, etc. Further, the system can be
expanded to include not only lane lines, but also road sign
recognition as well as lane detection on rural or lesser
travelled roads.
ACKNOWLEDGEMENT
We would like to give special thanks to our guide Prof. Vivek
Ramakrishnan for mentoring us throughout our project.
Also, we are thankful to the faculty of Electronics and
Telecommunication Engineering Department at Atharva
College of Engineering (University of Mumbai) for assisting
and helping us with our work whenever needed.
REFERENCES
[1] Jianfeng Zhao, Bodong Liang, and Qiuxia Chen, "The key
technology toward the self-driving car.", International
Journal of Intelligent Unmanned Systems, Vol. 6, No. 1
(2018).
[2] Mohamed Aly, “Real Time Detection of Lane Markers in
Urban Streets.”, IEEE Intelligent Vehicles Symposium,

Eindhoven University of Technology, Eindhoven, The
Netherlands (2008).
[3] https://docs.opencv.org/4.x/
[4] Hongwei Guo, “A Simple Algorithm for fitting a gaussian
Function,” IEEE Signal Processing Magazine, September
2011.
[5] Y. Wang, E. K. Teoha, and D. Shen., “Lane detection and
tracking using B-Snake.”, Image and Vision Computing 22,
pp: 269-28 (2004).
[6] https://www.cse.unr.edu/~bebis/CS791E/Notes/AreaP
rocess.pdf
[7] Raman Shukla et al., “Lane Detection System in Python
using OpenCV,” IJIRT, Volume 8 Issue 1, June 2021.
[8] F. Mariut, C. Fosalau and D. Petrisor, “Lane Mark
Detection Using Hough Transform.", IEEE International
Conference and Exposition on Electrical and Power
Engineering, pp. 871 – 875 (2012).
[9] K. Ghazali, R. Xiao, and J. Ma, “Road Lane Detection
Using H-Maxima and Improved Hough Transform.”, Fourth
International Conference on Computational Intelligence,
Modelling and Simulation, pp. 2166-8531 (2012).

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