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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 Build autonomous vehicles using deep neural networks and behavior-cloning techniques

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781838646301
Length 332 pages
Edition 1st Edition
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Authors (3):
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Dr. S. Senthamilarasu Dr. S. Senthamilarasu
Author Profile Icon Dr. S. Senthamilarasu
Dr. S. Senthamilarasu
Balu Nair Balu Nair
Author Profile Icon Balu Nair
Balu Nair
Sumit Ranjan Sumit Ranjan
Author Profile Icon Sumit Ranjan
Sumit Ranjan
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: Deep Learning Foundation and SDC Basics
2. The Foundation of Self-Driving Cars FREE CHAPTER 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

DeepLabv3+

DeepLab is the semantic segmentation state-of-the-art model. In 2016, it was developed and open sourced by Google. Multiple versions have been released and many improvements have been made to the model since then. These include DeepLab V2, DeepLab V3, and DeepLab V3+.

Before the release of DeepLab V3+, we were able to encode multi-scale contextual information using filters and pooling operations at different rates; the newer networks could capture the objects with sharper boundaries by recovering spatial information. DeepLabv3+ combines these two approaches. It uses both the encoder-decoder and the spatial pyramid pooling modules.

The following diagram shows the architecture of DeepLabv3+, which consists of encoder and decoder modules: 

Fig 8.6: DeepLabV3+ architecture

Let's look at the encoder and decoder modules in more detail:

  • Encoder: In the encoder step, essential information from the input image is extracted using a pre-trained convolutional neural...
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