Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques (Paperback or Softback)

Ranjan, Sumit

ISBN 10: 1838646302 / ISBN 13: 9781838646301

Editore: Packt Publishing 8/14/2020, 2020

Riguardo questo articolo

Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques.

Codice articolo BBS-9781838646301

Dati bibliografici

• Titolo: Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques (Paperback or Softback)
• Editore: Packt Publishing 8/14/2020
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 1838646302
• ISBN 13: 9781838646301
• Rilegatura: Paperback or Softback
• Condizione: New
• Peso: 1,26 libbre
• Tipo di libro: Book

Descrizione articolo

Riassunto

Explore self-driving car technology using deep learning and artificial intelligence techniques and libraries such as TensorFlow, Keras, and OpenCV

Key Features

• 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.

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 architectures

Who this book is 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.

Table of Contents

1. The Foundation of Self-Driving Cars
2. Dive Deep into Deep Neural Networks
3. Implementing a Deep Learning Model using Keras
4. Computer Vision for Self-Driving Cars
5. Finding Road Markings using OpenCV
6. Improving the Image Classifier with CNN
7. Road Sign Detection using Deep Learning
8. The Principles and Foundations of Semantic Segmentation
9. Implementation of Semantic Segmentation
10. Behavior Cloning using Deep Learning
11. Vehicle Detection using OpenCV and Deep Learning
12. Next Steps

Informazioni sull?autore

Sumit Ranjan is a silver medalist in his Bachelor of Technology (Electronics and Telecommunication) degree. He is a passionate data scientist who has worked on solving business problems to build an unparalleled customer experience across domains such as, automobile, healthcare, semi-conductor, cloud-virtualization, and insurance.

He is experienced in building applied machine learning, computer vision, and deep learning solutions, to meet real-world needs. He was awarded Autonomous Self-Driving Car Scholar by KPIT Technologies. He has also worked on multiple research projects at Mercedes Benz Research and Development. Apart from work, his hobbies are traveling and exploring new places, wildlife photography, and blogging.

Dr. S. Senthamilarasu was born and raised in the Coimbatore, Tamil Nadu. He is a technologist, designer, speaker, storyteller, journal reviewer educator, and researcher. He loves to learn new technologies and solves real world problems in the IT industry. He has published various journals and research papers and has presented at various international conferences. His research areas include data mining, image processing, and neural network.

He loves reading Tamil novels and involves himself in social activities. He has also received silver medals in international exhibitions for his research products for children with an autism disorder. He currently lives in Bangalore and is working closely with lead clients.