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Applied Deep Learning and Computer Vision for Self-Driving Cars

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

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781838646301
Length 332 pages
Edition 1st Edition
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Authors (3):
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Dr. S. Senthamilarasu Dr. S. Senthamilarasu
Author Profile Icon Dr. S. Senthamilarasu
Dr. S. Senthamilarasu
Balu Nair Balu Nair
Author Profile Icon Balu Nair
Balu Nair
Sumit Ranjan Sumit Ranjan
Author Profile Icon Sumit Ranjan
Sumit Ranjan
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Table of Contents (18) Chapters Close

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

Introduction to computer vision

Computer vision is a science that is used to make computers understand what is happening within an image. Some examples of the use of computer vision in self-driving cars are the detection of other vehicles, lanes, traffic signs, and pedestrians. In simple terms, computer vision helps computers understand images and videos, and determines what the computer is seeing in the surrounding environment.

The following screenshot shows how a human sees the world:

Fig 4.1: Human eye interpretation

In the preceding screenshot, we can see that humans see using their eyes. The visual information captured by their eyes is then interpreted in the brain, enabling the individual to conclude that the object is a bird. Similarly, in computer vision, the camera takes the role of the human eye and the computer takes the role of the brain, as shown in the following screenshot:

Fig 4.2: Computer interpretation 

Now the question is, what process actually happens in computer...

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