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Autonomous Car:Deep Learning & Computer Vision for Beginners

Autonomous cars: Deep Learning and Computer Vision Using Python & OpenCV on Raspberry Pi
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Content

Platform: Udemy
Video: 6h 27m
Language: English
Next start: On Demand

Table of contents

Description

This is course is involves both the hardware and the software part for building your custom car
Topics Which Will be Covered in the Course are
Hardware Part :
•Raspberry Pi Setup with Raspbian
•Raspberry pi and Laptop VNC Setup
•Hardware GPIO Programming
•Led Controlling with Python Code
•Motor Control
•Camera Interfacing Video Feed

Software Part :
•Video Processing Pipeline setup
•Lane Detection with Computer Vision Techniques
•Sign Detection using Artificial Deep Neural Network
•Sign Tracking using Optical Flow
•Control

Course Flow (Self-Driving [Development Stage])
We will quickly get our car running on Raspberry Pi by utilizing 3D models ( provided in the repository) and car parts bought from links provided by instructors. After that, we will interface raspberry Pi with Motors and the camera to get started with Serious programming.

Then by understanding the concept of self-drive and how it will transform our near future in the field of transportation and the environment. Then we will perform a case study of a renowned brand in self-driving (Tesla) ;).After that, we will put forward our proposal of which (autonomous driving level) self-driving vehicle do we want to build.
The core development portion of the course will be divide into two parts. In each of this portion and their subsection, we will look into different approaches. program them and perform an analysis. In the case of multiple approaches for each section, we will do a comparative analysis to sort out which approach best suits our project requirements.
1) Detection: responsible for extracting the most information about the environment around the SDV
     Here we will understand how to tackle a large problem by breaking it down into smaller more manageable problems e.g in the case of Detection. we will divide it into 4 targets
       a) Segmentation
       b) Estimation
       c) Cleaning
       d) Data extraction
2) Control: actions will be performed based on the information provided by the detection module.
     Starting by defining the targets of this module and then implementation of these targets such as
       a) Lane Following
       b) Obeying Road Speed Limits
In the end, we will combine all the individual components to bring our Self Driving (Mini – Tesla) to life. Then a Final Track run along with analysis will be performed to understand its achievement and shortcoming.
We will conclude by describing areas of improvement and possible features in the future version of the Self-driving (Mini-Tesla)
Hardware Requirements
•Raspberrypi 3b or greater
•Geared Dc motors 12V (single)
•12V lipo Battery
•Base of car + steering mechanism
Software Requirements
•Python 3.6
•Opencv 4.2
•TensorFlow
•Motivated mind for a huge programming Project

– This course is only supported for  Raspberry pi 3B and 3B+ , for other version of raspberry pi we do not guide how to install Tensorflow.
– Before buying take a look into this course Github repository  or message

• ( if you do not want to buy get the code at least and learn from it 🙂 )

You will learn

✓ Build your own Self Driving Car (Mini-Tesla) ????
✓ Learn (Divide and Conquer Approach)???? of solving complex problems i.e. Detection -> (Localization + Classification)
✓ Segment Lane Lines using Computer Vision ???? techniques i.e. Canny Edge detection & Color Thresholding
✓ Localize objects in images based on specific shapes using Algorithms i.e. ShapeApproxPoly and HoughCircles ????
✓ Estimate ???? Straight Line Trajectories Using Hough Lines and Curved Lanes using Custom Algo.
✓ Learn about Artificial Neural Networks and why Convolutional Neural Network are best ???? for classification in Images.
✓ Learn to identify the right ???? Algorithm in OpenCV and how to tweak it to your requirements
✓ Build, Train and deploy ???? Custom CNN model (Deep Learning) for classifying Signs.????
✓ Profile/Time ⌚ your program using cProfile in Python ????
✓ Compare ????️‍♂️ S.O.A Tracking techniques available in OpenCV and identify most suitable for project requirements
✓ Optimize ???? your Code using simple but very effective IP techniques and threading
✓ Make SDV ???? Navigate Autonomously in Custom Track and also obey road speed limits????
✓ Understand how to extract actionable data ???? from images
✓ Gain all the knowledge ???? required to enter to more advance versions of (SDV series) upcoming courses to come… :)
✓ Brief Overview of SSD for Sign ???? Detection and why its not the solution for every Object Det. problem

Requirements

• Basic Python programming and OpenCV
• 3D model’s STL files printing

This course is for

• Beginner Programmers looking for an exciting project in the upcoming domains
• Enthusiastic students wanting to get their feet wet in multidisciplinary projects
• Engineers wanting to embark in the fields of Computer Vision, Artificial Intelligence and Robotics
• Self Driving Cars Enthusiasts looking to build one of their own
Computer Vision Engineer
Computer Engineer with experience as a Computer Vision Engineer. Areas of interest include Computer Vision, Artificial Intelligence, and Robotics.
Deeply inspired by the methodology of learning together to utilize the above-mentioned fields to benefit humanity. Motivated to teach aspiring students, programmers, and engineers and introduce them to the ever-growing power of these fields and how they can leverage them to achieve their goals.
Platform: Udemy
Video: 6h 27m
Language: English
Next start: On Demand

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