Autonomous Car:Deep Learning & Computer Vision for Beginners

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

4.05 (36 reviews)
Udemy
platform
English
language
Hardware
category
instructor
431
students
6.5 hours
content
Aug 2023
last update
$69.99
regular price

What 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

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

  • Ackerman Drive car

  • 12V lipo Battery

  • Servo Motor

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 :) )

Content

Introduction to Hardware Design of Mini Tesla

How we are going to build the Hardware
How to Get The Resources for this Course
Setting up Raspberry PI
Turning On Raspberry PI Wireless Access Through VNC
Installing VNC on android phone
Hardware Programming on Raspberry PI
Led Blink Code Output
Constructing The Car
Car Designing and Parts buying Guide
Getting the Track ready for Mini Tesla
How Our Motor Works
Programming Motor Controlling
Analyzing Motor Controlling Output
Camera Interfacing with RPI
Get The Slides

Software Introduction

Welcome
*Required* How to Get The Resources for this Course?
Section Preview
Algorithms Overview
What is Self Drive?
Case Study (Tesla)
Our Proposal

Process Breakdown

Section Intro
Detection
Control

Detection

Section Intro
Lane : Intro
Lane : Segmentation (Edge) - Theory
Lane : Segmentation (Edge) - Codeflow
Lane : Segmentation (Edge) - Analysis
Lane : Segmentation (Color) - Theory
Lane : Segmentation (Color) - Codeflow
Lane : Segmentation (Color) - Analysis
Lane : Segmentation [Comparative Analysis]
Lane : Estimation [Why Estimation?]
Lane : Estimation (Hough Lines) - Theory
Lane : Estimation (Hough Lines) - Codeflow
Lane : Estimation (Hough Lines) - Analysis
Lane : Estimation (Custom) - Theory
Lane : Estimation (Custom) - Codeflow
Lane : Estimation (Custom) - Analysis
Lane : Estimation [Comparative Analysis]
Lane : Estimation [Code Setup (Chosen)]
Lane : Cleaning (Step 1) - Theory
Lane : Cleaning (Step 1) - Codeflow
Lane : Cleaning (Step 2) - Theory
Lane : Cleaning (Step 2) - Codeflow
Lane : Cleaning [Code Setup]
Lane : Cleaning [Analysis]
(Bonus) Lane : Optimizations101 [Why Profiling?]
(Bonus) Lane : Optimizations101 [Profilers in Python]
(Bonus) Lane : Optimizations101 [Optimization Using cProfile]
(Bonus) Lane : Optimizations101 [Threading]
Lane : Data Extraction - Theory
Lane : Data Extraction - Codeflow
Lane : Data Extraction - Code Completion
Lane : Complete Analysis
Signs : Goal
Signs : SSD
Signs : What's the Alternative ?
Signs : CourseFlow
Signs : Localization (ShapeDetection) - Codeflow
Signs : Localization (ShapeDetection) - Analysis
Signs : Localization (HoughCircles) - Theory
Signs : Localization (HoughCircles) - Codeflow
Signs : Localization (HoughCircles) - Analysis
Signs : Localization [Comparative Analysis]
Signs : Classification (CNN) - Theory
Signs : Classification (CNN) - Understanding Keras Layers
Signs : Classification (CNN) - Building Our Custom CNN
Signs : (Localization + Classification) - Analysis
Signs : Tracking - Why Tracking?
Signs : Tracking (CSRT) - Theory
Signs : Tracking (CSRT) - Codeflow
Signs : Tracking (MeanShift) - Theory
Signs : Tracking (MeanShift) - Codeflow
Signs : Tracking (CSRT & Meanshift) - Analysis
Signs : Tracking (OpticalFlow) - Theory
Signs : Tracking (OpticalFlow) - Codeflow
Signs : Tracking (OpticalFlow) - Analysis
Signs : Tracking - [Comparative Analysis]
Signs : Codeflow [Final]
Signs : Analysis [Final[

Control

Goal
Courseflow
A) Lane Following - Theory
A) Lane Following - Codeflow
B) Obeying Speed Limits
B) Obeying Speed Limits - Codeflow
Analysis [Final]

Self Driving Car

Putting Everything Together
Analysis [Track Run]

Concluding

Drawbacks And Improvements
Closing Remarks

Screenshots

Autonomous Car:Deep Learning & Computer Vision for Beginners - Screenshot_01Autonomous Car:Deep Learning & Computer Vision for Beginners - Screenshot_02Autonomous Car:Deep Learning & Computer Vision for Beginners - Screenshot_03Autonomous Car:Deep Learning & Computer Vision for Beginners - Screenshot_04

Reviews

Nguyen
June 19, 2022
Earlier in the learning process, I had a bit of difficulty because the course did not explain each piece of code carefully and felt not very good, but after thinking about it, to explain the meaning of each piece of code may take as long as a whole lot of time. more courses. So I feel that this course has come here very well and the teachers are very enthusiastic. This makes me very happy and satisfied with the course. Thank you very much sir.
Hassan
August 25, 2021
Good course, The lectures were short, concise and to the point which many courses lack. The information provided in the lectures was well explained without any extra detail, which is good because extra information can sometimes lead to loss of interest during the lecture. learning to build the hardware was itself a challenge and a great learning experience and interactive notes and code hierarchical flow helped in better understanding and saved time as well. Not for complete beginners thou.
Syed
August 15, 2021
Much better approach to the real learning and building a self driven car. I have enjoyed my journey with you guys. Modular approach with the coding part is really helpful in understanding the hierarchy. Course outline is finely defined for robust learning.

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4205524
udemy ID
7/26/2021
course created date
8/4/2021
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