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Applied Deep Learning and Computer Vision for Self-Driving Cars

You're reading from  Applied Deep Learning and Computer Vision for Self-Driving Cars

Product type Book
Published in Aug 2020
Publisher Packt
ISBN-13 9781838646301
Pages 332 pages
Edition 1st Edition
Languages
Authors (2):
Sumit Ranjan Sumit Ranjan
Profile icon Sumit Ranjan
Dr. S. Senthamilarasu Dr. S. Senthamilarasu
Profile icon Dr. S. Senthamilarasu
View More author details

Table of Contents (18) Chapters

Preface 1. Section 1: Deep Learning Foundation and SDC Basics
2. The Foundation of Self-Driving Cars 3. Dive Deep into Deep Neural Networks 4. Implementing a Deep Learning Model Using Keras 5. Section 2: Deep Learning and Computer Vision Techniques for SDC
6. Computer Vision for Self-Driving Cars 7. Finding Road Markings Using OpenCV 8. Improving the Image Classifier with CNN 9. Road Sign Detection Using Deep Learning 10. Section 3: Semantic Segmentation for Self-Driving Cars
11. The Principles and Foundations of Semantic Segmentation 12. Implementing Semantic Segmentation 13. Section 4: Advanced Implementations
14. Behavioral Cloning Using Deep Learning 15. Vehicle Detection Using OpenCV and Deep Learning 16. Next Steps 17. Other Books You May Enjoy

Summary

In this chapter, we learned about sensor fusion. Sensor fusion is the next step after collecting all of the sensor data. This book taught you about one of the most important types of sensors available: cameras. We also learned about deep learning networks that enable camera sensors to function, and are also useful for making predictions from the data generated by other types of sensors. Finally, we learned about Kalman filters.

The overall goal of this book was to introduce you to the field of SDCs and to help you prepare for a future in the industry.

Here is a quick summary of the chapters we covered in this book:

In Chapter 1, The Foundations of Self-Driving Cars, we addressed the complicated path of how SDCs are becoming a reality. We discovered that SDC technology has existed for decades. We learned how it has evolved and learned about advanced research on the topic as a result of modern computational power. We also learned about the advantages and disadvantages...

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