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Applied Deep Learning and Computer Vision for Self-Driving Cars

By : Sumit Ranjan, Dr. S. Senthamilarasu

4.3 (9)

Applied Deep Learning and Computer Vision for Self-Driving Cars

4.3 (9)

By: Sumit Ranjan, Dr. S. Senthamilarasu

Overview of this book

Thanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry. This book is a comprehensive guide to use deep learning and computer vision techniques to develop autonomous cars. Starting with the basics of self-driving cars (SDCs), this book will take you through the deep neural network techniques required to get up and running with building your autonomous vehicle. Once you are comfortable with the basics, you'll delve into advanced computer vision techniques and learn how to use deep learning methods to perform a variety of computer vision tasks such as finding lane lines, improving image classification, and so on. You will explore the basic structure and working of a semantic segmentation model and get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior-cloning and vehicle detection using OpenCV, transfer learning, and deep learning methodologies to train SDCs to mimic human driving. By the end of this book, you'll have learned how to implement a variety of neural networks to develop your own autonomous vehicle using modern Python libraries.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Reviews

Section 1: Deep Learning Foundation and SDC Basics

Section 1: Deep Learning Foundation and SDC Basics

Free Chapter

The Foundation of Self-Driving Cars

The Foundation of Self-Driving Cars

Introduction to SDCs

Benefits of SDCs

Advancements in SDCs

Challenges in current deployments

Building safe systems

The cheapest computer and hardware

Software programming

Fast internet

Levels of autonomy

Level 0 – manual cars

Level 1 – driver support

Level 2 – partial automation

Level 3 – conditional automation

Level 4 – high automation

Level 5 – complete automation

Deep learning and computer vision approaches for SDCs

LIDAR and computer vision for SDC vision

Summary

Dive Deep into Deep Neural Networks

Dive Deep into Deep Neural Networks

Diving deep into neural networks

Introduction to neurons

Understanding neurons and perceptrons

The workings of ANNs

Understanding activation functions

The threshold function

The sigmoid function

The rectifier linear function

The hyperbolic tangent activation function

The cost function of neural networks

Optimizers

Understanding hyperparameters

Model training-specific hyperparameters

Learning rate

Batch size

Number of epochs

Network architecture-specific hyperparameters

Number of hidden layers

Regularization

L1 and L2 regularization

Dropout

Activation functions as hyperparameters

TensorFlow versus Keras

Summary

Implementing a Deep Learning Model Using Keras

Implementing a Deep Learning Model Using Keras

Starting work with Keras

Advantages of Keras

The working principle behind Keras

Building Keras models

The sequential model

The functional model

Types of Keras execution

Keras for deep learning

Building your first deep learning model

Description of the Auto-Mpg dataset

Importing the data

Splitting the data

Standardizing the data

Building and compiling the model

Training the model

Predicting new, unseen data

Evaluating the model's performance

Saving and loading models

Summary

Section 2: Deep Learning and Computer Vision Techniques for SDC

Section 2: Deep Learning and Computer Vision Techniques for SDC

Computer Vision for Self-Driving Cars

Computer Vision for Self-Driving Cars

Introduction to computer vision

Challenges in computer vision

Artificial eyes versus human eyes

Building blocks of an image

Digital representation of an image

Converting images from RGB to grayscale

Road-marking detection

Detection with the grayscale image

Detection with the RGB image

Challenges in color selection techniques

Color space techniques

Introducing the RGB space

HSV space

Color space manipulation

Introduction to convolution

Sharpening and blurring

Edge detection and gradient calculation

Introducing Sobel

Introducing the Laplacian edge detector

Canny edge detection

Image transformation

Affine transformation

Projective transformation

Image rotation

Image translation

Image resizing

Perspective transformation

Cropping, dilating, and eroding an image

Masking regions of interest

The Hough transform

Summary

Finding Road Markings Using OpenCV

Finding Road Markings Using OpenCV

Finding road markings in an image

Loading the image using OpenCV

Converting the image into grayscale

Smoothing the image

Canny edge detection

Masking the region of interest

Applying bitwise_and

Applying the Hough transform

Optimizing the detected road markings

Detecting road markings in a video

Summary

Improving the Image Classifier with CNN

Improving the Image Classifier with CNN

Images in computer format

The need for CNNs

The intuition behind CNNs

Introducing CNNs

Why 3D layers?

Understanding the convolution layer

Depth, stride, and padding

Depth

Stride

Zero-padding

ReLU

Fully connected layers

The softmax function

Introduction to handwritten digit recognition

Problem and aim

Loading the data

Reshaping the data

The transformation of data

One-hot encoding the output

Building and compiling our model

Compiling the model

Training the model

Validation versus train loss

Validation versus test accuracy

Saving the model

Visualizing the model architecture

Confusion matrix

The accuracy report

Summary

Road Sign Detection Using Deep Learning

Road Sign Detection Using Deep Learning

Dataset overview

Dataset structure

Image format

Loading the data

Image exploration

Data preparation

Model training

Model accuracy

Summary

Section 3: Semantic Segmentation for Self-Driving Cars

Section 3: Semantic Segmentation for Self-Driving Cars

The Principles and Foundations of Semantic Segmentation

The Principles and Foundations of Semantic Segmentation

Introduction to semantic segmentation

Understanding the semantic segmentation architecture

Overview of different semantic segmentation architectures

U-Net

SegNet

Encoder

Decoder

PSPNet

DeepLabv3+

E-Net

Summary

Implementing Semantic Segmentation

Implementing Semantic Segmentation

Semantic segmentation in images

Semantic segmentation in videos

Summary

Section 4: Advanced Implementations

Section 4: Advanced Implementations

Behavioral Cloning Using Deep Learning

Behavioral Cloning Using Deep Learning

Neural network for regression

Behavior cloning using deep learning

Data collection

Data preparation

Model development

Evaluating the simulator

Summary

Vehicle Detection Using OpenCV and Deep Learning

Vehicle Detection Using OpenCV and Deep Learning

What makes YOLO different?

The YOLO loss function

The YOLO architecture

Fast YOLO

YOLO v2

YOLO v3

Implementation of YOLO object detection

Importing the libraries

Processing the image function

The get class function

Draw box function

Detect image function

Detect video function

Importing YOLO

Detecting objects in images

Detecting objects in videos

Summary

Next Steps

Next Steps

SDC sensors

Camera

RADAR

Ultrasonic sensors

Odometric sensors

LIDAR

Introduction to sensor fusion

Kalman filter

Summary

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Kalman filter

One of the most popular sensor fusion algorithms is the Kalman filter. It is used to merge the data from various autonomous vehicle sensors. The Kalman filter was invented in 1960 by Rudolph Kalman. It is used to track navigation signals, as well as phones and satellites.

The Kalman filter was used during the first manned mission to land on the moon (the Apollo 11 Mission) for communication between staff on Earth and the crew on the shuttle/rocket.

The main application of the Kalman filter is data fusion, which is used to estimate the state of a dynamic system in the present, past, and future. It can be used to monitor a moving pedestrian's location and velocity over time, and also to quantify their associated uncertainty. In general, the Kalman filter consists of two iterative steps:

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The state of a system is calculated using a Kalman filter and is denoted as x. This vector is composed of a position (p)and a velocity (v), while the measure of uncertainty...

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