Computer Vision

Lane Detection using Machine Learning

Machine Learning is everywhere. Nowadays, automatic driving cars are into action and being used by lots of people. These automatic driving cars need something that can detect lanes or even moving objects. Here Machine Learning comes into action. Machine learning is used in medical science too. In this case, we will be using deep learning to detect lanes.

Read more..

Lane Detection using Machine Learning project Looking to build projects on Computer Vision?:

Computer Vision Kit will be shipped to you and you can learn and build using tutorials. You can start for free today!

1. Sixth Sense Robot

2. 3 Computer Vision Projects (Combo Course)

3. Computer Vision - Text Scanner

4. Computer Vision Based Mouse

5. Computer Vision Based Smart Selfie


Objective:

The goal of the project is to detect lanes. Lane detection is a boon when the craze of automatic driving cars is increasing with passing days.

People can find lanes very easily, but computers struggle to find a lane in various weather conditions, like in snowy mornings or whether in a rainy evening or even on a dark night. 

Concepts Used:

  1. Basic of Python programming language (try using Python 3 or above)
  2. Basics of Machine learning algorithms
  3. Deep Learning (optional)

There are two approaches to doing this.

  1. Hough Transform
  2. Spatial CNN

Latest projects on Computer Vision

Want to develop practical skills on Computer Vision? Checkout our latest projects and start learning for free


What is a Hough Transform?

Most of the lanes where you drive will notice they are very much straight. This helps drivers to drive cars smoothly and maintain an average speed. So here, we will first detect straight lanes. We will feed these straight lanes through the camera through edge detection and feature extraction. We have to use computer vision techniques here. For that, we will be using OpenCV here.

What is Spatial CNN?

This method works only if the road is straight. If the lane has some curvatures or turns, this method will not work.

Implementations:

1. The first and important thing that we need is data. This will help you to create your own dataset, that in turn will lead to an accurate model. You should create a large dataset. You can collect data from your own area. 

2. Set up the environment. Install all packages and libraries that you need. The code is given below.

pip install OpenCV-python

3. Feed your video (the data that you have collected).

Video = cv.VideoCapture(“input.mp4”)

4. Now you need to detect the edges of the lane. We will have to use the canny detector to detect any edges that are present in your input dataset.

5. Canny detector can do four things. i)Noise cancellation, ii) intensity gradient, iii)non-maximum suppression and iv) Hysteresis thresholding.

6. Now we have to segment the lane area. We will discard the parts that are not needed. This will help us to create a model that can detect the roads accurately. This will help us to narrow down the area of interest and work with the only part that we need.

7. We will apply Hough transformation to detect the left line and the right line. These are the two-lane boundaries.

8. Now we need to visualize our data. 

9. Now open the terminal and run your file. That’s it. You have successfully made a model that can detect lanes.

Conclusion:

This process includes many handcrafted processes. You can also use a spatial CNN model which is helpful too. 


How to build Computer Vision projects Did you know

Skyfi Labs helps students learn practical skills by building real-world projects.

You can enrol with friends and receive kits at your doorstep

You can learn from experts, build working projects, showcase skills to the world and grab the best jobs.
Get started today!


Kit required to develop Lane Detection using Machine Learning:
Technologies you will learn by working on Lane Detection using Machine Learning:
Land Detection using Machine Learning
Skyfi Labs Last Updated: 2021-07-02





Join 250,000+ students from 36+ countries & develop practical skills by building projects

Get kits shipped in 24 hours. Build using online tutorials.

More Project Ideas on Computer-vision

Hybrid Median Filter for Noise Removal in Digital Images
Image Processing based fire detection
Library Management System using SQL and C++
Detection of Asthma Trigger using Zigbee
Image retrieval
Number Plate Detector
Sign Language Reader
Optical Character Recognition(OCR)
Face recognition gate
Surveillance Camera using Raspi Cam and Android App
Template matching using Computer vision
Motion detector using Computer vision
Streaming Video to a web-page using Open CV
Computer vision based rescue robot
Smart gesture control for mobile phone using machine learning
Image Processing based ball tracking robot
Emotion recognition using image processing
Computer vision based self-recharging robot
Disease Prediction using Image Processing
Forgery detection using Image Processing
Invisible Cloak using Open CV and Python
Currency Recognition System using Image Processing
Cartooning an Image using Open CV
Develop Sign Language Translator with Python
Develop an Audio Sign Language Translator Using ML
Image classifier for identifying cat vs dogs using CNN and python
Age Prediction using Image Processing
Color detection
Gender and Age Detection using OpenCV
Car model recognition using Image Processing
Checking driver behavior with Raspberry Pi
Dimension Estimation using Image Processing
Typing Robot
Detection of Underground broken pipes
Computer vision based Smart Selfie
Computer vision based text scanner
Cancer detection using image processing
Develop A Sixth Sense Robot With Arduino
Lane Detection using Machine Learning

Subscribe to receive more project ideas

Stay up-to-date and build projects on latest technologies