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Applied Deep Learning and Computer Vision for Self-Driving Cars

Applied Deep Learning and Computer Vision for Self-Driving Cars

By : Sumit Ranjan, Dr. S. Senthamilarasu
4.3 (9)
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Applied Deep Learning and Computer Vision for Self-Driving Cars

Applied Deep Learning and Computer Vision for Self-Driving Cars

4.3 (9)
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)
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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

Perspective transformation

Perspective transformation is an important aspect of programming self-driving cars. Perspective transformation is more complicated than affine transformation. In perspective transformation, we use a 3 x 3 transformation matrix to transform images from the 3D world into 2D images. 

An example of perspective transformation is shown in Fig 4.61. In the following screenshot, we can see the tilted chessboard, and once the perspective transform is applied, the board is transformed into a normal chessboard with a top-down view. This has numerous applications in the field of self-driving cars, as roads have many objects that require perspective transformation to be processed:

Fig 4.61: Perspective transform using a chessboard

Now we will implement perspective transformation to a traffic signboard using the OpenCV library:

  1. We will first import the matplotlib (mpimg and pyplot), numpy, and openCV libraries:
In[1]: import cv2
In[2]: import numpy as...

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