Realtime Road Lane Detection

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Realtime Road Lane Detection
1,2,3,4 Student, Computer Dept., PVGCOET, Savitribai Phule Pune University, India
5Prof, Computer Dept., PVGCOET, Savitribai Phule Pune University, India
---------------------------------------------------------------***-------------------------------------------------------------
Abstract- These are many cases of people dying each year in
road accidents due to driver’s lack of attention. Over the
years many new technologies have helped to overcome such
cases. Detecting lanes have proven to be useful for drivers.
The main purpose of lane detection system is to avoid such
accidents. These systems are designed to detect the lane
marks and to warn the driver if the vehicle tends to depart
from the lane. Detecting lanes is an important aspect of an
intelligent transport system. Future is of driverless car
technologies; this system is not only useful for drivers but also
for driverless vehicles. Few challenges that this system might
face are varying road conditions, weather conditions. In
previous years different approaches for lane detection were
proposed and demonstrated. In this paper, a comprehensive
review of the literature in lane detection techniques is
presented. The main objective of this paper is to discover the
limitations of the existing lane detection methods.
Key words: Lane detection, Intelligent Transport
Systems
1. INTRODUCTION
With the drasticincrease of urban traffic, safety of traffics
has become more mandatory. Breaking the lane causes
about more than 30% of accidents on highways, and these
are due to inattention or fatigued driver. Thus, a system
that provides analert message to drivers if any danger
having great potential to save lives. Advanced driver
assistance systems are technologies that are designed to aid
drivers while driving a car (ADAS). Many systems such as
adaptive cruise control, collision avoidance system, night
vision, blind spot detection and traffic sign detection are a
part of ADAS [1]. Lane departure system is also a part of
this category. The system’s goal is to identify the lane marks
and notify the driver if the vehicle tends to leave or change
the lane. Lane detection is the process to detect lane
markers on road and then present these locations to an
advanced system. In intelligent transportation systems [2],
intelligent vehicles cooperate with smart infrastructure to
achieve a safer environment and better traffic conditions.
The applications of a lane detecting system could be as
simple as pointing out lane locations to the driver on
anexternal display, to more complex tasks such as
predicting a lane change in the instant future to avoid
collisions with other vehicles. Other interfaces included that
can detect lanes are cameras, laser range images, LIDAR
and GPS devices[3].
This paper is organised as follows. Section II presents the
Related Work. Section III presents the Literature Survey.
Section IV describes the Gaps in the existing Systems.
Section V presents the Proposed System. Section VI gives
the detailed System Implementation Details. The last
section concludes the article.
2. RELATED WORK
2.1 Image processing
An important part of lane detection is image processing
in order to achieve an accurate result. The first step in
image processing is to convert the image into grayscale i.e.,
converting the images into gray ones. The first step in image
processing is grayscale processing, which converts color
images into gray ones. Grayscale processing is used to carry
out the next step, binarization, in which the gray image is
turned into a black-and-white image. Many algorithms were
proposed on how to implement binarization. A new
algorithm was proposed [9] in 2017 to improve the
traditional algorithm by using an adaptive threshold
instead, which improves binarization performance for old
images.
2.2 Lane detection
The first step of the lane detection stage is to identify
the range of detected objects in the image, also known as
the region of interest (ROI). There are two types of ROI in
images, namely static ROI and dynamic ROI. In [10] an
image's near vision field is divided into a near vision field
area, a far vision field area, and the sky field is divided into
static ROIs. The sky field accounts for 5/12 of the image,
and the far vision field accounts for half the near vision
field. Most algorithms utilise the ROI-processed picture
after getting itedge detection to extract essential road lane
characteristics from the picture that has been provided.
Canny edge detection is the most widely used edge
detection technique (CED). Hough transfer is utilised for
lane line detection once the pixels with large brightness
variations have been marked. [10] employed standard
Hough transfer in its algorithm. The approach determines
the lane lines for the straight line by using the starting point
and finishing point. For the straight line, the method uses
the beginning point and the end in order to determine the
lane lines. In the case of a curve, the approach calculates the
bending direction of the curve on the right base and
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022 www.irjet.net p-ISSN: 2395-0072
Samyak Shah#1, Abhishek Jagtap#2 , Rutik Darda#3, Sakshi Kamble#4, Prof A.M.Bhadgale#5

determines the waveform using the least square fit. Wei et
al. [14] used points of disappearance and some pixels
around points, the classic Hough transfer was improved.
The Hough Transform is a technique for locating lines by
identifying all of their points. This can be performed by
representing a line as points. These points are represented
as lines/sinusoidal (depending on Cartesian/Polar co-
ordinate system). If multiple lines/sinusoidal pass through
the point, we can deduce that these points lie on the same
line.
2.3.Lane departure recognition
While the gap between the car and the left lane line is much
less than the distance between the vehicle and
the right lane line, the car starts to flow to left, otherwise,
the automobile starts off evolved to flow proper. The
deviation distance of the car, in truth, can be calculated by
way of the ratio of the deviation distance and driveway
inside the picture. Whilst the deviation distance exceeds a
selected cost, LDWS works and warns the driving force to
adjust again to the safe using range inside the driveway.
3. LITERATURE SURVEY
3.1. Data Collection and Processing Methods for the
Evaluation of Vehicle Road Departure Detection
Systems
In recent years, off-road collision avoidance / mitigation
off-road collision detection systems (RDDS) have been
developed and installed in some production vehicles. To
support and provide standardized and objective
performance assessments for RDDS, this paper describes
the development of the data acquisition and data post-
processing systems for testing RDDSs. Seven parameters
are used to describe road departure test scenarios. The
overall structure and specific components of the data
acquisition system and data post-processing system for
evaluating the vehicle RDDS will be developed and
presented.The sensing system and data post-processing
system captured all required signals and accurately
displayed the testing vehicle's motion profile, according to
the results. The suggested data collection system's
effectiveness is demonstrated by test track testing under
various scenarios.
3.2. A Lane Detection Method for Lane Departure
Warning System
The vision-based Lane Departure WarningSystem (LDWS)
is an effective way to prevent Single Vehicle Road
Departure accident. In fact, a lot of complex noise makes it
very difficult to quickly and accurately identify a lane, that
is, to define a lane, a set of image processing method which
can give results fast and accurately in the non-ideal
conditions is the primary work. This document proposes a
lane detection method for the Lane Departure Warning
System. Experimental comparisons determine the Canny
algorithm as the edge detection method and the Hough
transform as the efficient method for air wire detection. To
meet real-time requirements, the Region of Interest (ROI) is
defined to reduce noise and speed up for accurate ramping.
Finally, experimental results show that this lane detection
method can efficiently and accurately extract lane
information from captured road images.
3.3. Real-time illumination invariant lane detection for
lane departure warning system
Lane detection is an important factor in improving driving
safety. This paper proposes a real-time, lighting-invariant
lane detection method for the Lane Departure Warning
System. There are three primary components to it. First, it
detects vanishing points based on the voting map and
defines an adaptive region of interest (ROI) to reduce
computational complexity. Second, we take advantage of
lane colours' unique nature to achieve illumination
invariant lane marker candidate detection. Finally, use a
clustering technique to find the main track from the lane
marker candidates. In the event of a lane departure
situation, our system will send a warning signal to the
driver. Experimental results show satisfactory performance
with an average detection rate of 93% under a variety of
lighting conditions. In addition, the overall process requires
only 33MS per frame.
3.4. A Robust Lane Detection Method Based on
Vanishing Point Estimation Using the Relevance of Line
Segments
This article proposes a robust lane detection method based
on vanishing point estimation. Estimating the vanishing
point helps identify the lane because the parallel lines in the
2D projected image converge to the vanishing point.
However, for images with complex backgrounds, it is not
easy to determine the vanishing point correctly. Thus, a
robust vanishing point estimation method is proposed that
uses a probabilistic voting procedure based on intersection
points of line segments extracted from an input image. The
proposed voting function is defined by the strength of the
line segment, which represents the relevance of the
extracted line segments. Then select a candidate lane
segment, taking into account geometric constraints. Finally,
Fig1. Lane Departure Recognition

the host trace is detected using the proposed score function
designed to remove outliers for the candidate line. In
addition, the detected host traces are improved using
interframe similarity that takes into account the position
consistency of the detected host traces and the estimated
vanishing points of consecutive frames. In addition, we
suggest using a look-up table to reduce the computational
cost of the vanishing point estimation process.
Experimental results show that the proposed method
efficiently estimates the vanishing point and recognizes
lanes in a variety of environments.
3.5. A Framework for Camera-Based Real-Time Lane
and Road Surface Marking Detection and Recognition
In this paper Vision based, integrated framework based on
spatio-temporal incremental clustering coupled with curve
fitting and Grassmann manifold learning technique for lane
detection is used. It is only restricted by the type of road
surface markings present in the training data.
3.6. New Lane Detection and tracking strategy based on
vehicle forward monocular camera
A new strategy for lane detection and tracking that is
suitable as a functional prerequisite for using DAS features
such as lane departure warning and lane departure
warning. Here the visibility of the lane markings was
compromised by several factors, such as the reflectivity of
the lane, the camera’s glare, and the presence of shadows
and the deterioration of the paint.
3.7. A Portable Vision-Based Real-Time Lane Departure
Warning System: Day and Night
Here embedded advanced RISC machines (ARM)-based
real-time Lane Departure Warning System is proposed. The
limitation being this system will poorly perform if there is
no sufficient illumination.
3.8. Automatic Detection and Classification of Road
Lane Markings Using Onboard Vehicular Cameras
It is a new approach for road lane classification using an
onboard camera using Bayesian classifier but it limits the
rapid detection of transitions and removal of isolated
misdetections.
3.9. Lane Departure Identification for Advanced Driver
Assistance
This approach reduces the computational time required for
the lane departure estimation and reduces the false
warnings but does not estimate the real-world coordinates
of a vehicle with respect to both lane boundaries.
3.10. Robust Road Lane Detection from shape and color
feature fusion for Vehicle Self-Localization
It used a system to extract the region of interest, perform
Hough transformation and calculate the vehicle position.
The only limitation is the vehicle's controller limitations
arise for a maximum speed of 70 km/h in sharp turns.
3.11. Lane Detection Algorithm using Vanishing point
It proposed a system using Canny edge detection, kalman
algorithm to extract the region of interest but same
limitation as mentioned in the above paper, The vehicle's
controller limitations arise for a maximum speed of 70
km/h in sharp turns.
3.12. Lane Detection Algorithm using Vanishing point
Lane detection method using the vanishing point according
to the perspective feature of the camera. Here the limitation
is that image processing takes a long time. It is a major
factor affecting the system’s real-time feature.
4. GAPS IN EXISTING LITERATURE
A literature search found that most existing literature
ignores one of the following:
 The survey has shown that the existing methods
provides good accuracy for high quality images but
sometimes provide poor results for poor environmental
conditions like fog, haze, noise, dust etc.
 The majority of existing solutions work well for straight
lanes but not so well for curved roads.
 Because most lane detection approaches are based on
the conventional Hough transform, they can be tweaked
to improve accuracy even more.
5. THE PROPOSED WORK
In many proposed systems [4], lane detection consists of
finding specific primitives such as lane markings on the
surface of painted roads. Parked and moving vehicles, poor
quality lines, trees, buildings, shadows from other vehicles,
sharp curves, irregular lane shapes, converging lanes,
lettering and other markings on the road, unusual road
surface materials Various challenges, such as different
grades, cause lane detection problems. There has been
active research on lane detection and a wide variety of
algorithms of various representations, detection and
tracking techniques, and modalities have been proposed
[5].

Single vehicle road departure (SVRD) accidents account
for a high percentage of all highway accidents. In 2006,
SVRD accidents accounted for about 20% of road accidents
and 40% of fatalities across the United States, according to
AASHTO (American Association of State Highway Traffic
Authority) statistics. According to a Mercedes-Benz
research report on road accidents, the reasons for this can
be divided into five columns: leaving the road (equivalent
to 19% of all traffic accidents), changing lanes and merging
(equivalent to 4%). Crossings (equivalent to 29%), rear-end
collisions (equivalent to 26%). , And other reasons account
for about 22%. The study also shows that more than 80% of
accidents are associated with unintentional or dozing
drivers, and that a 0.5 second warning can avoid 60% of
accidents. If a one-second warning is given, 90% of
accidents can be averted. Lane Departure Warning System
(LDWS) is one of the most important aspects of Intelligent
Vehicle (IV) technology, which is an active safety system to
prevent unintended lane departure that can lead single
vehicle roadway departure, lane change/merge, and
rollover crashes. LDWS uses a forward-looking monocular
camera to capture lane information and determine the
vehicle's position with respect to the lane. If a lane change
occurs and the vehicle's turn signal is not in use, LDSW
warns the driver when the vehicle exceeds a certain speed
threshold.
Many approaches have been applied to lane detection,
which can be classified as either feature-based or model-
based [7-8]. Feature-based methods detect lanes by low-
level features like lane-mark edges [9-11]. The feature-
based methods are highly dependent on clear lane-marks,
and suffer from weak landmarks, noise, and occlusions. The
model-based method represents lanes as a kind of curved
model that can be determined by some important
geometric parameters. The model-based method is less
sensitive to weak lane appearance features and noise than
the feature-based method. However, a model created for
one scene may not work in another, making the process less
adaptable. In addition, the best possible estimation of
model parameters requires the application of a relatively
time-consuming iterative error minimization algorithm.
The general procedure for lane detection is to first
capture an image of the roadway using a camera mounted
on the vehicle. The image is then converted to grayscale to
minimize processing time. Second, the presence of noise in
the image interferes with correct edge detection. Therefore,
it is necessary to apply a filter such as a bilateral filter,
Gabor filter, or trilateral filter to remove noise. It then uses
an edge detector to generate an edge image by preserving
the edges with an automatic threshold canny filter. Then,
after detecting the edge, the edge image is sent to the line
detector to create the left and right lane boundary
segments. Lane boundary scan uses the information in the
edge image captured by the Hough transform to perform
the scan. The scan returns a series of points on the left and
right. Finally, a pair of hyperbolas fits into these data points
and represents the boundaries of the lane. For visualization
purposes the hyperbolas are displayed on the original
colour image.
5.1. Raspberry PI zero module
The Raspberry Pi is a popular single board computer (SBC)
because it is a complete computer packaged in a single
circuit board. The Raspberry Pi 3 and its predecessors are
likely recognisable to many people. These are the most
recognized form factors today. The Raspberry Pi is offered
in an even smaller form factor. With the introduction of the
Raspberry Pi Zero, it's now possible to integrate an entire
computer into a smaller project. The Raspberry Pi Zero
Wireless, the latest iteration of the Zero line with an
integrated WiFi module, is described in this guide. These
steps should work for most versions and form factors of the
Raspberry Pi, but the focus is on the Pi Zero W.
Fig 2. Flowchart

5.2. Image Processing
After acquiring the image information in front of the
vehicle, the lane marking features are first extracted by
image processing, and then the lane is adapted from these
features. In reality, light and evasive manoeuvres and a
variety of complex noises make it difficult to detect lanes
quickly and accurately. As a result, establishing a series of
image processing methods is one of the key technologies of
LDWS that can provide fast and accurate results under the
condition of the acquired image information. This is not
ideal.
 Edge Detection
 Linear Model Fitting
 Setting of the Region of Interest (ROI)
5.3. Overview of Project Modules
Identifying the lanes of a road is a very common task for a
human driver. This is important to keep the vehicle in the
lane. This is also a very important task that self-driving cars
must perform. And a very simple lane detection pipeline is
possible with simple computer vision technology. This
report describes a simple pipeline that can be used for
simple lane detection using Python and OpenCV.
We have provided following features into our system:
1. Hardware can capture the images
2. Hardware can detect the road lanes
3. Hardware can give notification via buzzer/speaker
when driver crossed the lane
4. Hardwar can give notification via buzzer/speaker when
zebra crossing is detected
5. Hardware can detect any Road Signs and give
notification via buzzer/speaker
6. IMPLEMENTATION DETAILS
6.1. Lane Detection Pipeline:
1. Convert original image to grayscale.
2. Darkened the grayscale image (this help in reducing
contrast from discoloured regions of road)
3. Convert original image to HLS colour space.
4. To make yellow mask, isolate yellow from HLS. (For
yellow lane markings)
Fig 3. System Architecture
Fig 4. Class Diagram
Fig 5. Sequence Diagram

5. Isolate white from HLS to get white mask. (For white
lane markings)
6. Bit-wise OR yellow and white masks to get common
mask.
7. Bit-wise AND mask with darkened image.
8. Apply slight Gaussian Blur.
9. Apply canny Edge Detector (adjust the thresholds—
trial and error) to get edges.
10. Define Region of Interest. This helps in weeding out
unwanted edges detected by canny edge detector.
11. Retrieve Hough lines.
12. Consolidate and extrapolate the Hough lines and draw
them on original image.
Fig 6. Original Image
6.1.1 Convert to grayscale
Converting original image to grayscale has its benefit. We
need to detect yellow and white lanes, and changing the
original image to grayscale,improve the lanes' contrast with
the road. increases the contrast of lanes with respect to
road.
6.1.2. Darken the grayscale image
This is done with the intent of reducing contrast of
discoloured patches of the road.
6.1.3. Convert original image to HLS colour space
The original image is RGB, but you should also look at other
color spaces such as HSV and HLS. If you look at them side
by side, it's easy to see that in the HLS color space, you can
get better color contrast from the streets. This may help in
better colour selection and in turn lane detection.
Fig 6. Original Image
(A) Convert original image to HLS
(B) Convert original image to HLS
colour space
Fig 7. Converted to Grayscale
Fig 8. Darken the grayscale image

Colour Selection
6.1.3.1. Colour Selection
To acquire the mask between the threshold and hold value,
we utilise OpenCV's inRange function. We can determine the
threshold range after some trial and error.
For yellow mask:
 Hue value was used between 10 and 40.
 We use higher saturation value(100–255) to avoid
yellow from hills.
For white mask:
 We use higher lightness value (200–255) for white
mask.
We bit-wise OR both masks to get combined mask.
Below images show combined mask being bit-wise AND
with darkened image.
(A) Bit-wise AND with darkened image
(B) Bit-wise AND with darkened image
6.1.4 Gaussian Blur
Gaussian blur (Gaussian smoothing) is a pre-processing
step used to reduce (or smooth) image noise. Use this pre-
processing step to remove many of the detected edges and
keep only the most prominent edges in the image.
GaussianBlur from OpenCV expect kernel size(odd value)
for blurring. After trying some values, we used 7.
6.1.5. Canny Edge Detection
Now we apply Canny Edge Detection to these Gaussian
blurred images. The algorithm Canny Edge Detection
recognises edges based on gradient change. Despite the fact
that picture smoothing with default kernel size 5 is the first
stage of Canny Edge detection, we still use explicit Gaussian
blur in the prior phase. The other steps in Canny Edge
detection include:
 Finding Intensity Gradient of the Image
 Non-maximum Suppression
 Hysteresis Thresholding
(C) Convert original image to HLS
(A) Canny Edge Detection

(B) Canny Edge Detection
6.1.6. Select Region of Interest
Many non-lane edges are detected even after applying
canny edge detection. Regions of interest are polygons that
define the regions in the image that the edges are interested
in.
Note that the image's co-ordinate origin is in the top-left
corner, with rows increasing top-down and columns
increasing left-right.
The assumption is that the camera will remain in the same
position and that the lanes will be flat, allowing us to "guess"
the region of interest.
6.1.7. Hough Transformation Lines Detection
The Hough transform is a technique for finding a line by
identifying every point on the line. This is done by
representing the line as a point. Points are also represented
as lines / signs (depending on the Cartesian / polar
system). If multiple lines / sine waves pass through a point,
we can conclude that these points are on the same line.
Fig 10. Hough Transformation and Line Detection
7. CONCLUSION AND FUTURE WORK
Lane recognition technology plays an important role in
intelligent transportation systems. In this paper, we
considered how to detect lanes. Most of them resulted in
inaccurate results. Therefore, further improvements can be
done to enhance the results. Soon, one can modify the
existing Hough Transformation so that it can measure both
the curved and straight roads. Various steps should be
taken to improve the results in different environmental
conditions like sunny day, foggy day, rainy day etc.The
proposed system and system implementation give the
detailed implementation which will result in high accuracy
while tackling the limitations of the other systems
discussed in the literature survey.The lane detection shall
have proved to be an efficient technique to prevent
accidents using Intelligent Transportation Systems. Our
algorithm will increase the detection accuracy of the
system. From the results of the experiment, our method
(C) Canny Edge Detection
(D) Canny Edge Detection
Fig 9. Selecting Region of Interest

gets lane change and warning goals.It is expected that the
proposed algorithm shall detect more than 98% details in
the image with good condition, and over 96% details in
less-than-ideal weather conditions. A database can be used
to store the record of already visited potholes, blockages on
the road which may have been visited in the past while
passing through the same route.And aim to reduce the
number of accidents caused on the roads and also to
improve the seriousness of such accidents.
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