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

Introducing Sobel 

The gradient-based method based on the first-order derivatives is called the Sobel edge detector. The Sobel edge detector calculates the first-order derivatives of the image separately for the x axis and y axis. Sobel uses two 3 x 3 kernels that convolve over the original image to calculate the derivatives. For image A, Gx and Gy are two images that represent the horizontal and vertical derivative approximations:

The * character indicates the 2D signal processing convolution operation.

The Sobel kernels compute the gradient with smoothing, as it can be decomposed a product of the averaging and differentiation kernels.

Sobel computes the gradient using smoothing. For example, * can be written as follows:

Here, the x-coordinate shows an increase in a right direction, and the y-coordinate shows an increase in a downward direction.

The resulting gradient approximations at each point in the image can be merged...