This document describes a lane detection software project using image processing to help drivers see lane lines in foggy conditions. The software uses the Hough transform algorithm to detect lanes by identifying lines of consistent color. It provides a real-time display of the lane boundaries and direction indicators to assist drivers. The project aims to decrease accidents caused by low visibility by enabling drivers to easily detect lanes even in foggy environments.

1. LANE DETECTION IN FOG
USING
IMAGE PROCESSING
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2. INTRODUCTION
● While driving in early morning the main problem is fog. Due to
fog our visibility of road decreases and accidents are occurs.
Fog slows down vehicle due to less visibility and waste our
time. So resolving this problem we create a software of Lane
Detection for vehicles to help driver detect the lane in fog in or
anytime they want.
● We created the basic version of Lane Detection to only help
driver to detect lanes and giving a track bar to adjust the road
size in our software.
● This software is based on ‘Hough Transform’ which is the best
way to detect lanes. There are many ways to detect lanes but
Hough Transform is best and easily understandable way which
we easily implemented.
● This software is very useful and cross platform working which is

3. OBJECTIVE OF PROJECT
● The main objective of Lane Detection in fog is to
decrease the accidents due to less visibility of road
in early morning.
● This software detect the lane with tracking with a
colour to easily detection and predict and analyse
the road direction which is very useful to peoples
while driving.

4. FEATURES
● Define a threshold to get a binary edge map.
● Divide the image into blocks Classify each block as lane
mark.
● Compensate perspective by calculating “bird’s-eye
view” Identify lanes by predefined color.
● Train a neural network to detect lanes.
● Search for low-high-low intensity pattern along image
rows.

5. ADVANTAGES
● Its advantages of Hough transform and Edge detection are
easy to use, low cost and also effective in the field of
detecting lines.
● Experimental results reveal the efficiency of the
performance of the lane detection algorithm in various
environments.
● It give good performance on straight line road.

6. DISADVANTAGES
● Cannot fit good roads(polynomial regression has not
impemented)
● Bad quality of lines sometimes lane detection fails to detect
the lanes.
● Reflection of tree on road lane fails to detect the lines.

7. FLOW CHART FOR ACTUAL LANE
DETECTION ALGORITHM

8. OUTPUT

10. + : TURN RIGHT

11. - : TURN LEFT

12. CONCLUSION
 Hence we solve the problem of lane detection using
Hough transform algorithm in image processing
techniques with Python’s Opencv package.
 Also we predict and analyze the direction of path in
dynamic way.
 While any problem occurs in detection of path we gives
a Track-bar for adjusting the width of Lane.

13. THANK YOU