Book Image

Applied Deep Learning and Computer Vision for Self-Driving Cars

By : Sumit Ranjan, Dr. S. Senthamilarasu
Book Image

Applied Deep Learning and Computer Vision for Self-Driving Cars

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.
Table of Contents (18 chapters)
1
Section 1: Deep Learning Foundation and SDC Basics
5
Section 2: Deep Learning and Computer Vision Techniques for SDC
10
Section 3: Semantic Segmentation for Self-Driving Cars
13
Section 4: Advanced Implementations

Digital representation of an image

Now we will see how to represent an image digitally. We will start with grayscale images. A grayscale picture is one where shades of gray are the only colors in the image. The grayscale image is a simple form of the image, and so is easy to process with multiple applications. These are also known as black and white images. Let's look at an image of a car. This image is stored digitally in the form of pixels:

Fig 4.6: Grayscale image 

Each pixel has a number in it that ranges from 0 to 255. If a pixel's value is zero, that means the color is black. If its value is 255, it will be white. As this number increases, so does the pixel's brightness. In the following screenshot, we can see that the black pixels contain the number 0 and the white pixels contain the number 255. We can see pixels that are gray too, which occurs between the numbers 0 and 255. This is essentially how we represent the image in a decimal...