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

Cover
Title Page
Copyright and Credits
About Packt
Contributors
Table of Contents
Preface
Section 1: Deep Learning Foundation and SDC Basics
Chapter 1: The Foundation of Self-Driving Cars
	Introduction to SDCs
		Benefits of SDCs
		Advancements in SDCs
	Challenges in current deployments 
		Building safe systems
		The cheapest computer and hardware 
		Software programming
		Fast internet
	Levels of autonomy 
		Level 0 – manual cars
		Level 1 – driver support
		Level 2 – partial automation
		Level 3 – conditional automation
		Level 4 – high automation
		Level 5 – complete automation
	Deep learning and computer vision approaches for SDCs
		LIDAR and computer vision for SDC vision 
	Summary
Chapter 2: Dive Deep into Deep Neural Networks
	Diving deep into neural networks
		Introduction to neurons
	Understanding neurons and perceptrons
	The workings of ANNs
	Understanding activation functions
		The threshold function
		The sigmoid function
		The rectifier linear function
		The hyperbolic tangent activation function
	The cost function of neural networks
	Optimizers
	Understanding hyperparameters
		Model training-specific hyperparameters
			Learning rate
			Batch size
			Number of epochs
		Network architecture-specific hyperparameters 
			Number of hidden layers 
			Regularization
				L1 and L2 regularization
				Dropout
			Activation functions as hyperparameters
	TensorFlow versus Keras
	Summary
Chapter 3: Implementing a Deep Learning Model Using Keras
	Starting work with Keras
		Advantages of Keras 
		The working principle behind Keras
		Building Keras models
			The sequential model
			The functional model
		Types of Keras execution
	Keras for deep learning
	Building your first deep learning model 
		Description of the Auto-Mpg dataset
		Importing the data
		Splitting the data
		Standardizing the data
		Building and compiling the model
		Training the model
		Predicting new, unseen data
		Evaluating the model\'s performance
		Saving and loading models
	Summary
Section 2: Deep Learning and Computer Vision Techniques for SDC
Chapter 4: Computer Vision for Self-Driving Cars
	Introduction to computer vision
		Challenges in computer vision
		Artificial eyes versus human eyes 
	Building blocks of an image
		Digital representation of an image
		Converting images from RGB to grayscale
		Road-marking detection 
			Detection with the grayscale image
			Detection with the RGB image
		Challenges in color selection techniques
	Color space techniques
		Introducing the RGB space
		HSV space
		Color space manipulation 
	Introduction to convolution
		Sharpening and blurring
	Edge detection and gradient calculation
		Introducing Sobel 
		Introducing the Laplacian edge detector
		Canny edge detection
	Image transformation
		Affine transformation
		Projective transformation
		Image rotation 
		Image translation
		Image resizing 
		Perspective transformation
		Cropping, dilating, and eroding an image
		Masking regions of interest
		The Hough transform
	Summary
Chapter 5: Finding Road Markings Using OpenCV
	Finding road markings in an image
		Loading the image using OpenCV
		Converting the image into grayscale
		Smoothing the image
		Canny edge detection
		Masking the region of interest
		Applying bitwise_and
		Applying the Hough transform
		Optimizing the detected road markings
	Detecting road markings in a video
	Summary
Chapter 6: Improving the Image Classifier with CNN
	Images in computer format
		The need for CNNs
		The intuition behind CNNs
	Introducing CNNs
		Why 3D layers?
		Understanding the convolution layer
		Depth, stride, and padding 
			Depth
			Stride 
			Zero-padding
		ReLU
		Fully connected layers
			The softmax function
	Introduction to handwritten digit recognition
		Problem and aim
		Loading the data
		Reshaping the data
		The transformation of data
		One-hot encoding the output
		Building and compiling our model
		Compiling the model 
		Training the model
		Validation versus train loss
		Validation versus test accuracy
		Saving the model
		Visualizing the model architecture
		Confusion matrix 
		The accuracy report
	Summary
Chapter 7: Road Sign Detection Using Deep Learning
	Dataset overview
		Dataset structure
		Image format
	Loading the data
	Image exploration
	Data preparation
	Model training
	Model accuracy
	Summary
Section 3: Semantic Segmentation for Self-Driving Cars
Chapter 8: The Principles and Foundations of Semantic Segmentation
	Introduction to semantic segmentation
	Understanding the semantic segmentation architecture
	Overview of different semantic segmentation architectures
		U-Net
		SegNet
			Encoder
			Decoder
		PSPNet
		DeepLabv3+
		E-Net
	Summary
Chapter 9: Implementing Semantic Segmentation
	Semantic segmentation in images
	Semantic segmentation in videos
	Summary
Section 4: Advanced Implementations
Chapter 10: Behavioral Cloning Using Deep Learning
	Neural network for regression
	Behavior cloning using deep learning
		Data collection
		Data preparation
		Model development
		Evaluating the simulator
	Summary
Chapter 11: Vehicle Detection Using OpenCV and Deep Learning
	What makes YOLO different?
	The YOLO loss function
	The YOLO architecture 
		Fast YOLO
		YOLO v2
		YOLO v3
	Implementation of YOLO object detection
		Importing the libraries
		Processing the image function
		The get class function
		Draw box function
		Detect image function
		Detect video function
		Importing YOLO
		Detecting objects in images
		Detecting objects in videos
	Summary
Chapter 12: Next Steps
	SDC sensors
		Camera
		RADAR
		Ultrasonic sensors
		Odometric sensors
		LIDAR 
	Introduction to sensor fusion
	Kalman filter
	Summary
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Index