Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques
€28.99€19.99
Aug 2020332 pages1st EditioneBook
Subscription
€14.99 MonthlyPlay Sample Audio
eBook
Subscription
€14.99 Monthly
Key benefits
- Build and train powerful neural network models to build an autonomous car
- Implement computer vision, deep learning, and AI techniques to create automotive algorithms
- Overcome the challenges faced while automating different aspects of driving using modern Python libraries and architectures
Book description
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.Who is this book for?
If you are a deep learning engineer, AI researcher, or anyone looking to implement deep learning and computer vision techniques to build self-driving blueprint solutions, this book is for you. Anyone who wants to learn how various automotive-related algorithms are built, will also find this book useful. Python programming experience, along with a basic understanding of deep learning, is necessary to get the most of this book.What you will learn
Implement deep neural network from scratch using the Keras library Understand the importance of deep learning in self-driving cars Get to grips with feature extraction techniques in image processing using the OpenCV library Design a software pipeline that detects lane lines in videos Implement a convolutional neural network (CNN) image classifier for traffic signal signs Train and test neural networks for behavioral-cloning by driving a car in a virtual simulator Discover various state-of-the-art semantic segmentation and object detection architecturesWhat do you get with eBook? 
Instant access to your Digital eBook purchase
Download this book in EPUB and PDF formats
AI Assistant (beta) to help accelerate your development
Access this title in our online reader with advanced features
DRM FREE - Read whenever, wherever and however you wantProduct Details
Publication date :
Aug 14, 2020
Length :
332 pages
Edition :
1st Edition
Language :
English
ISBN-13 :
9781838646301
Category :
Languages :
Concepts :
Product Details
Publication date :
Aug 14, 2020
Length :
332 pages
Edition :
1st Edition
Language :
English
ISBN-13 :
9781838646301
Category :
Languages :
Concepts :
What do you get with eBook?
Instant access to your Digital eBook purchaseDownload this book in EPUB and PDF formatsAI Assistant (beta) to help accelerate your developmentAccess this title in our online reader with advanced featuresDRM FREE - Read whenever, wherever and however you wantProduct Details
Publication date :
Aug 14, 2020
Length :
332 pages
Edition :
1st Edition
Language :
English
ISBN-13 :
9781838646301
Category :
Languages :
Concepts :
Frequently bought together
€32.99
€22.99
€32.99
€22.99
€39.99
€27.98
€39.99
€27.98
€28.99
€19.99
€28.99
€19.99
Total
€ 0.00
Total
€ 0.00
Table of Contents
18 Chapters
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
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
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
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 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
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
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
Recommendations for you
Similar Content in Data
People who bought this also bought
Authors (2)
Get free access to Packt library with over 7500+ books and video courses for 7 days!
FAQs
Where there is an eBook version of a title available, you can buy it from the book details for that title. Add either the standalone eBook or the eBook and print book bundle to your shopping cart. Your eBook will show in your cart as a product on its own. After completing checkout and payment in the normal way, you will receive your receipt on the screen containing a link to a personalised PDF download file. This link will remain active for 30 days. You can download backup copies of the file by logging in to your account at any time.
If you already have Adobe reader installed, then clicking on the link will download and open the PDF file directly. If you don't, then save the PDF file on your machine and download the Reader to view it.
Please Note: Packt eBooks are non-returnable and non-refundable.
Packt eBook and Licensing When you buy an eBook from Packt Publishing, completing your purchase means you accept the terms of our licence agreement. Please read the full text of the agreement. In it we have tried to balance the need for the ebook to be usable for you the reader with our needs to protect the rights of us as Publishers and of our authors. In summary, the agreement says:
- You may make copies of your eBook for your own use onto any machine
- You may not pass copies of the eBook on to anyone else
If you want to purchase a video course, eBook or Bundle (Print+eBook) please follow below steps:
- Register on our website using your email address and the password.
- Search for the title by name or ISBN using the search option.
- Select the title you want to purchase.
- Choose the format you wish to purchase the title in; if you order the Print Book, you get a free eBook copy of the same title.
- Proceed with the checkout process (payment to be made using Credit Card, Debit Cart, or PayPal)
- If you experience a problem with using or installing Adobe Reader, the contact Adobe directly.
- To view the errata for the book, see www.packtpub.com/support and view the pages for the title you have.
- To view your account details or to download a new copy of the book go to www.packtpub.com/account
- To contact us directly if a problem is not resolved, use www.packtpub.com/contact-us
Our eBooks are currently available in a variety of formats such as PDF and ePubs. In the future, this may well change with trends and development in technology, but please note that our PDFs are not Adobe eBook Reader format, which has greater restrictions on security.
You will need to use Adobe Reader v9 or later in order to read Packt's PDF eBooks.
- You can get the information you need immediately
- You can easily take them with you on a laptop
- You can download them an unlimited number of times
- You can print them out
- They are copy-paste enabled
- They are searchable
- There is no password protection
- They are lower price than print
- They save resources and space
Packt eBooks are a complete electronic version of the print edition, available in PDF and ePub formats. Every piece of content down to the page numbering is the same. Because we save the costs of printing and shipping the book to you, we are able to offer eBooks at a lower cost than print editions.
When you have purchased an eBook, simply login to your account and click on the link in Your Download Area. We recommend you saving the file to your hard drive before opening it.
For optimal viewing of our eBooks, we recommend you download and install the free Adobe Reader version 9.
