Autonomous Car:Deep Learning & Computer Vision for Beginners

Autonomous Car:Deep Learning & Computer Vision for Beginners

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 :) )

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

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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

Rating: 4.2

Level: All Levels

Duration: 6.5 hours

Instructor: Haider Najeeb