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You're reading from Applied Deep Learning and Computer Vision for Self-Driving Cars Build autonomous vehicles using deep neural networks and behavior-cloning techniques

Product type Paperback

Published in Aug 2020

Publisher Packt

ISBN-13 9781838646301

Length 332 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Computer Vision

Authors (3):

Dr. S. Senthamilarasu

Balu Nair

Sumit Ranjan

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Table of Contents (18) Chapters

Preface

1. Section 1: Deep Learning Foundation and SDC Basics

2. The Foundation of Self-Driving Cars FREE CHAPTER

3. Dive Deep into Deep Neural Networks

4. Implementing a Deep Learning Model Using Keras

5. Section 2: Deep Learning and Computer Vision Techniques for SDC

6. Computer Vision for Self-Driving Cars

7. Finding Road Markings Using OpenCV

8. Improving the Image Classifier with CNN

9. Road Sign Detection Using Deep Learning

10. Section 3: Semantic Segmentation for Self-Driving Cars

11. The Principles and Foundations of Semantic Segmentation

12. Implementing Semantic Segmentation

13. Section 4: Advanced Implementations

14. Behavioral Cloning Using Deep Learning

15. Vehicle Detection Using OpenCV and Deep Learning

16. Next Steps

17. Other Books You May Enjoy

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The Hough transform

The Hough transform is one of the most important topics of computer vision. It is used in feature extraction and image analysis. The Hough transform was invented in 1972 by Richard Duda and Peter Hart, and it was originally called the generalized Hough transform. In general, the technique is used to find instances of objects that are not perfectly within a certain class by means of a voting procedure.

We can use the Hough transform along with region of interest masking. We will see an example of the detection of road markings in Chapter 5, Finding Road Markings Using OpenCV, using the Hough transform and region of interest masking together.

We will learn about the Hough transform in more detail with the drawing of a 2D coordinate space of x and y inside a straight line, as shown in Fig 4.74.

We know that the equation of a straight line is . The straight line has two parameters, m and c, and we are currently plotting it...

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Authors (2)

Sumit Ranjan

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.

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Dr. S. Senthamilarasu

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.

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