Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques
暫譯: 應用深度學習與電腦視覺於自駕車：使用深度神經網絡和行為複製技術構建自動駕駛車輛

Ranjan, Sumit, Senthamilarasu, S.

Applied Deep Learning and Computer Vision for Self-Driving Cars: Build autonomous vehicles using deep neural networks and behavior-cloning techniques
  • 出版商: Packt Publishing
  • 出版日期: 2020-08-14
  • 售價: $2,010
  • 貴賓價: 9.5 折 $1,910
  • 語言: 英文
  • 頁數: 332
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1838646302
  • ISBN-13: 9781838646301
  • 相關分類: DeepLearning、自駕車、Computer Vision

    • 海外代購書籍(需單獨結帳)

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商品描述

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.

商品描述(中文翻譯)

**探索使用深度學習和人工智慧技術及庫（如 TensorFlow、Keras 和 OpenCV）的自駕車技術**

#### 主要特點

- 建立和訓練強大的神經網絡模型以構建自動駕駛汽車
- 實施計算機視覺、深度學習和人工智慧技術來創建汽車算法
- 克服在自動化駕駛不同方面時所面臨的挑戰，使用現代 Python 庫和架構

#### 書籍描述

由於最近的一些突破，自駕車技術現在已成為人工智慧領域的一個新興主題，並將數據科學家的重點轉向構建將改變汽車行業的自動駕駛汽車。本書是使用深度學習和計算機視覺技術開發自動駕駛汽車的綜合指南。

本書從自駕車（SDCs）的基本概念開始，將帶您了解構建自動駕駛車輛所需的深度神經網絡技術。一旦您對基本概念感到熟悉，您將深入研究先進的計算機視覺技術，並學習如何使用深度學習方法執行各種計算機視覺任務，例如尋找車道線、改善圖像分類等。您將探索語義分割模型的基本結構和工作原理，並掌握使用語義分割檢測汽車的技巧。本書還涵蓋了使用 OpenCV 的行為克隆和車輛檢測等先進應用，以及轉移學習和深度學習方法來訓練 SDC 模型以模仿人類駕駛。

在本書結束時，您將學會如何實現各種神經網絡，以使用現代 Python 庫開發自己的自動駕駛車輛。

#### 您將學到什麼

- 使用 Keras 庫從零開始實現深度神經網絡
- 理解深度學習在自駕車中的重要性
- 掌握使用 OpenCV 庫進行圖像處理的特徵提取技術
- 設計一個檢測視頻中車道線的軟體管道
- 實現一個卷積神經網絡（CNN）圖像分類器，用於交通信號標誌
- 通過在虛擬模擬器中駕駛汽車來訓練和測試行為克隆的神經網絡
- 發現各種最先進的語義分割和物體檢測架構

#### 本書適合誰

如果您是深度學習工程師、人工智慧研究人員，或任何希望實施深度學習和計算機視覺技術來構建自駕車藍圖解決方案的人，本書適合您。任何想學習各種與汽車相關的算法是如何構建的讀者，也會發現本書有用。具備 Python 編程經驗以及對深度學習的基本理解是充分利用本書的必要條件。

作者簡介

Sumit Ranjan is a silver medalist in his Bachelor of Technology (Electronics and Telecommunication) degree. He is a passionate data scientist who has worked on solving business problems to build an unparalleled customer experience across domains such as, automobile, healthcare, semi-conductor, cloud-virtualization, and insurance.

He is experienced in building applied machine learning, computer vision, and deep learning solutions, to meet real-world needs. He was awarded Autonomous Self-Driving Car Scholar by KPIT Technologies. He has also worked on multiple research projects at Mercedes Benz Research and Development. Apart from work, his hobbies are traveling and exploring new places, wildlife photography, and blogging.

Dr. S. Senthamilarasu was born and raised in the Coimbatore, Tamil Nadu. He is a technologist, designer, speaker, storyteller, journal reviewer educator, and researcher. He loves to learn new technologies and solves real world problems in the IT industry. He has published various journals and research papers and has presented at various international conferences. His research areas include data mining, image processing, and neural network.

He loves reading Tamil novels and involves himself in social activities. He has also received silver medals in international exhibitions for his research products for children with an autism disorder. He currently lives in Bangalore and is working closely with lead clients.

作者簡介(中文翻譯)

Sumit Ranjan 是電子與電信學士學位的銀牌得主。他是一位充滿熱情的數據科學家，致力於解決商業問題，以在汽車、醫療保健、半導體、雲虛擬化和保險等領域打造無與倫比的客戶體驗。

他在構建應用機器學習、計算機視覺和深度學習解決方案方面擁有豐富的經驗，以滿足現實世界的需求。他曾獲得 KPIT Technologies 的自動駕駛汽車獎學金。他還在梅賽德斯-奔馳的研究與開發部門參與了多個研究項目。除了工作，他的興趣包括旅行和探索新地方、野生動物攝影以及寫博客。

Dr. S. Senthamilarasu 出生並成長於印度泰米爾納德邦的科印巴托。他是一位技術專家、設計師、演講者、故事講述者、期刊審稿人、教育者和研究人員。他熱愛學習新技術，並在資訊科技行業解決現實世界的問題。他已發表多篇期刊和研究論文，並在多個國際會議上進行報告。他的研究領域包括數據挖掘、圖像處理和神經網絡。

他喜歡閱讀泰米爾小說，並積極參與社會活動。他的研究產品曾在國際展覽中獲得銀獎，專為自閉症兒童設計。他目前居住在班加羅爾，並與主要客戶密切合作。

目錄大綱

  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

目錄大綱(中文翻譯)


  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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