Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques - Tapa blanda

Sinopsis

Explore self-driving car technology using deep learning and artificial intelligence techniques and libraries such as TensorFlow, Keras, and OpenCV

Key Features

• Build and train powerful neural network models to build an autonomous car
• Implement computer vision, deep learning, and AI techniques to create automotive algorithms
• Overcome the challenges faced while automating different aspects of driving using modern Python libraries and architectures

Book Description

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.

What you will learn

• Implement deep neural network from scratch using the Keras library
• Understand the importance of deep learning in self-driving cars
• Get to grips with feature extraction techniques in image processing using the OpenCV library
• Design a software pipeline that detects lane lines in videos
• Implement a convolutional neural network (CNN) image classifier for traffic signal signs
• Train and test neural networks for behavioral-cloning by driving a car in a virtual simulator
• Discover various state-of-the-art semantic segmentation and object detection architectures

Who this book is for

If you are a deep learning engineer, AI researcher, or anyone looking to implement deep learning and computer vision techniques to build self-driving blueprint solutions, this book is for you. Anyone who wants to learn how various automotive-related algorithms are built, will also find this book useful. Python programming experience, along with a basic understanding of deep learning, is necessary to get the most of this book.

Table of Contents

1. The Foundation of Self-Driving Cars
2. Dive Deep into Deep Neural Networks
3. Implementing a Deep Learning Model using Keras
4. Computer Vision for Self-Driving Cars
5. Finding Road Markings using OpenCV
6. Improving the Image Classifier with CNN
7. Road Sign Detection using Deep Learning
8. The Principles and Foundations of Semantic Segmentation
9. Implementation of Semantic Segmentation
10. Behavior Cloning using Deep Learning
11. Vehicle Detection using OpenCV and Deep Learning
12. Next Steps

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