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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
Vehicle Detection Using OpenCV and Deep Learning

Object detection is one of the important applications of computer vision used in self-driving cars. Object detection in images means not only identifying the kind of object but also localizing it within the image by generating the coordinates of a bounding box that contains the object. We can summarize object detection as follows:

An example of object detection can be seen in the following image:

Fig 11.1: Object detection

Here, you can see that the biker is detected as a person and that the bike is detected as a motorbike

In this chapter, we are going to use OpenCV and You Only Look Once (YOLO) as the deep learning architecture for vehicle detection. Due to this, we'll learn about the state-of-the-art image detection algorithm known as YOLO. YOLO can view an image...

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