Object Detection with Deep Learning Models: Principles and Applications

✍ Scribed by S. Poonkuntran, Rajesh Kumar Dhanraj, Balamurugan Balusamy

• Publisher: CRC Press/Chapman & Hall
• Year: 2022
• Tongue: English
• Leaves: 275
• Category: Library

✦ Synopsis

Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval.

The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks.

Features:

• A structured overview of deep learning in object detection.

• A diversified collection of applications of object detection using deep neural networks.

• Emphasize agriculture and remote sensing domains.

• Exclusive discussion on moving object detection.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Editors
List of Contributors
Chapter 1: Introduction:: Deep Learning and Computer Vision
1.1 Introduction to Deep Learning
1.1.1 Deep Learning
1.1.2 Machine Learning and Deep Learning
1.1.3 Types of Networks in Deep Learning
1.1.3.1 Connection Type of Networks
1.1.3.1.1 Static Feedforward Networks
1.1.3.1.2 Dynamic Feedback Neural Networks
1.1.3.2 Topology-based Neural Networks
1.1.3.2.1 Single-layer Neural Networks
1.1.3.2.2 Multilayer Neural Networks
1.1.3.2.3 Recurrent Neural Networks
1.1.3.3 Learning Methods
1.1.3.3.1 Supervised Learning
1.1.3.3.2 Unsupervised Learning
1.1.3.3.3 Reinforcement Learning
1.2 Convolutional Neural Networks
1.2.1 Description of Five Layers of General CNN Architecture
1.2.1.1 Input Layer
1.2.1.2 Convolutional Layer
1.2.1.3 Pooling Layer
1.2.1.4 Fully Connected Layers
1.2.1.5 Output Layer
1.2.2 Types of Architecture in CNN [ 9 ]
1.2.2.1 LeNet-5
1.2.2.2 AlexNet
1.2.2.3 ZFNet
1.2.2.4 GoogLeNet/Inception
1.2.2.5 VGGNet
1.2.2.6 ResNet
1.2.3 Applications of Deep Learning
1.3 Image Classification, Object Detection and Face Recognition
1.3.1 Dataset Creation
1.3.2 Data Preprocessing
1.3.3 Image Classification
1.3.4 Object Detection
1.3.5 Face Recognition
References
Chapter 2: Object Detection Frameworks and Services in Computer Vision
2.1 Neural Networks (NNs) and Deep Neural Networks (DNNs)
2.1.1 Neural Networks
2.1.2 Single-Layer Perceptron (SLP)
2.1.3 Multilayer Perceptron (MLP)
2.2 Activation Functions
2.2.1 Identity Function
2.2.2 Sigmoid Function
2.2.3 Softmax Function
2.2.4 Tanh Function
2.2.5 ReLU (Rectified Linear Unit) Function
2.3 Loss Functions
2.4 Convolutional Neural Networks
2.4.1 CNN Architecture and its Components
2.5 Image Classification Using CNN
2.5.1 LeNet-5
2.5.2 AlexNet
2.5.3 VGGNet
2.5.4 Inception and GoogLeNet
2.5.4.1 Inception Module
2.5.5 ResNet
2.5.5.1 Residual Block
2.6 Transfer Learning
2.6.1 Need for Transfer Learning
2.6.2 Transfer Learning Approaches
2.6.2.1 Pre-trained Network as a Classifier
2.6.2.2 Pre-trained Network as a Feature Extractor
2.6.2.3 Fine Tuning
2.7 Object Detection
2.7.1 Object Localization
2.7.1.1 Sliding Window Detection
2.7.1.2 Bounding Box Prediction
2.7.2 Components of Object Detection Frameworks
2.8 Region-Based Convolutional Neural Networks (R-CNNs)
2.8.1 R-CNN
2.8.2 Fast R-CNN
2.8.2.1 Components of Fast R-CNN
2.8.3 Faster R-CNN
2.8.4 YOLO Algorithm
2.8.5 YOLOv1 Object Detection Model
2.8.6 YOLO9000 Object Detection Model
2.8.7 YOLOv3 Object Detection Model
2.9 Computer Vision Application Areas
References
Chapter 3: Real-Time Tracing and Alerting System for Vehicles and Children to Ensure Safety and Security, Using LabVIEW
3.1 Introduction
3.2 Scope of the Chapter
3.3 System Requirements
3.3.1 Hardware Requirements
3.3.2 Software Requirements
3.4 Real-Time Tracing and Alerting System Environment
3.4.1 Image Acquisitions Module
3.4.2 Object Identification Module
3.5 System Architecture
3.6 Outline of Vehicle-tracking Processing
3.7 Overview of RFID
3.7.1 Implementation of RFID
3.8 Module
3.8.1 Vehicle Tracking Using LabVIEW
3.8.2 Student Tracking Using RFID
3.8.3 Alerting System
3.9 Conclusion
References
Chapter 4: Mobile Application-based Assistive System for Visually Impaired People: A Hassle-Free Shopping Support System
4.1 Introduction
4.1.1 Cataracts
4.1.2 Age-Related Macular Degeneration (AMD)
4.1.3 Diabetic Retinopathy
4.1.4 Glaucoma
4.2 Related Works
4.2.1 Item Identification
4.2.2 Barcodes
4.3 Proposed System
4.3.1 Barcode Capture
4.3.2 Barcode Detection
4.3.3 Barcode Pre-processing
4.3.4 Scan Distance
4.3.5 User Feedback System
4.3.6 Decoding of Barcode Image
4.3.7 Fetching Product Specification
4.3.8 Text-to-Speech Using Google TTS
4.4 Experimental Result and Discussion
4.4.1 Dataset Collection
4.5 Conclusion and Future Work
References
Chapter 5: Traffic Density and On-road Moving Object Detection Management, Using Video Processing
5.1 Introduction
5.1.1 Problem Definition
5.2 Literature Survey
5.3 Technical Concepts
5.3.1 Image Processing
5.3.2 Architecture Design
5.3.3 Background Registration
5.3.4 Color Identification
5.3.5 Data Flow Diagram for Detection Modules
5.3.6 Data Flow Diagram for Tracking Modules
5.3.7 Data Flow Diagram for Counting Modules
5.4 Proposed Methodology
5.4.1 Proposed Method Steps
5.5 Simulation and Result
5.6 Conclusion
References
Chapter 6: Automated Vehicle Number Plate Recognition System, Using Convolution Long Short-Term Memory Technique
6.1 Introduction
6.2 Literature Review
6.2.1 Related Work Related to License Plate Recognition Technology
6.2.2 Deep Learning-Based Work for Recognizing License Plates
6.3 Methodology
6.3.1 Convolutional LSTM
6.4 Experiments
6.5 Results
6.5.1 Parameters for Evaluation
6.5.2 Evaluation Metrics
6.5.3 Comparison Evaluation Fusion Model With Baseline Models
6.6 Conclusion
References
Chapter 7: Deep Learning-Based Indian Vehicle Number Plate Detection and Recognition
7.1 Introduction
7.2 Literature Survey
7.3 Proposed System
7.4 Experimentation & Results
References
Chapter 8: Smart Diabetes System Using CNN in Health Data Analytics
8.1 Introduction
8.1.1 What Is Big Data?
8.1.2 Analytics in Big Data
8.1.3 Healthcare – Big Data Analytics
8.1.3.1 Challenges
8.1.3.2 Developing Complexity of Healthcare Information
8.1.4 Big Data Framework
8.1.4.1 Cloud and Big Data
8.1.4.2 Open Source Arrangements for Big Data Information in Healthcare
8.2 Problem Identification
8.3 Proposed Solution
8.3.1 Smart Diabetes System
8.3.2 Objectives
8.3.2.1 Personalized Information Investigation Demonstration for Smart Diabetes
8.4 5G Smart Diabetes Model – Technologies
8.4.1 Fifth Generation Mobile Networks
8.4.2 Machine Learning Techniques
8.4.2.1 AI Applications in Healthcare
8.4.2.1.1 Discrete occasion simulation
8.4.2.1.2 Free-text doctor notes
8.4.3 How Convolution Neural Network Applies Here
8.4.4 Medical Big Data
8.4.4.1 Diabetes 1.0
8.4.4.2 Diabetes 2.0
8.4.5 Social Networking
8.4.6 Smart Clothing
8.5 Smart Diabetes Architecture
8.5.1 Smart Diabetes Design
8.5.2 Detection
8.5.3 Personalized Determination
8.5.4 Information Sharing
8.5.4.1 Social Space
8.5.4.2 Information Space
8.5.4.3 How Can Social and Information Space Be Combined?
8.5.5 System Sensor Architecture
8.5.5.1 How Does the Continuous Glucose Monitor (CGM) Work?
8.5.5.2 Phenomenal Highlights of a CGM
8.5.5.3 Unprecedented Necessities Required to Utilize a CGM
8.5.5.4 Who Can Utilize a CGM?
8.5.5.5 What Are the Benefits of a CGM?
8.5.5.6 What Are the Constraints of a CGM?
8.5.5.7 What Could Put Everything in Order for an Artificial Pancreas?
8.6 5G Smart Diabetes System Test Bed & Result
8.6.1 Information Collection from a Healing Community
8.6.2 Diet
8.6.3 Exercise
8.6.4 Sharing Information
8.6.5 The Test Bed of Machine Learning Calculations
8.6.6 Results
8.7 Conclusion
References
Chapter 9: Independent Automobile Intelligent Motion Controller and Redirection, Using a Deep Learning System
9.1 Introduction
9.2 Related Work
9.3 Existing System
9.4 Proposed Method: Two-tier Approach for AI Transportation Traffic Flow Administration
9.4.1 Optimization of Traffic Lights
9.5 Smart Redirected Path Use
9.6 Discussions and Results
9.6.1 First Layer
9.6.2 Second Layer
9.6.3 Third Layer
9.6.4 Fourth Layer
9.6.5 Fifth Layer
9.7 Conclusion
9.8 Acknowledgment
References
Chapter 10: Deep Learning Solutions for Pest Detection
10.1 Introduction
10.1.1 Object Detection
10.1.2 Deep Object Detection
10.1.2.1 Types of Deep Object Detection
10.1.3 Challenges in Object Detection
10.2 Advances in Agriculture
10.2.1 Smart Farming
10.2.2 Deep Learning in Agriculture
10.2.3 Automatic Pest Detection
10.2.4 Challenges in Automatic Pest Detection
10.2.4.1 Extrinsic Factors
10.2.4.2 Intrinsic Factors
10.2.4.3 Big Data Availability for Deep Detection
10.3 Novel Smart Intelligent System for Paddy Pest Detection
10.3.1 Related Work
10.3.2 Training Phase
10.3.2.1 Dataset Description
10.3.2.2 Classes Used in the Proposed Method
10.3.3 EfficientDet
10.3.4 Server Framework
10.3.5 Mobile Service Framework
10.3.6 Performance Metrics for Evaluation
10.3.6.1 Precision and Recall
10.3.6.2 Average Precision (AP)
10.3.6.3 Mean average Precision (mAP)
10.3.6.4 Precision-Recall Curve
10.3.6.5 Inference Speed
10.3.6.6 Service Time for User
10.4 Conclusion
References
Chapter 11: Deep Learning Solutions for Pest Identification in Agriculture
11.1 Introduction
11.2 Existing Literature
11.2.1 Disease Detection
11.2.2 Land Cover Identification
11.2.3 Classification of Plants
11.2.4 Precision Livestock Farming
11.2.5 Pest Recognition
11.3 Background Details
11.3.1 Deep Learning
11.3.2 Motivation of this Study
11.3.3 Contribution
11.3.3.1 Similarity of Different Types of Plant Disease
11.3.3.2 Steps Involved in Plant Disease Detection
11.3.3.3 Deep Learning in Tomato Diseases
11.3.3.4 Deep Learning in Potato Diseases
11.3.3.5 Deep Learning in Apple Diseases
11.3.3.6 Deep Learning Approaches for High Spectral Images in Agricultural Field
11.4 Conclusion
References
Chapter 12: A Complete Framework for LULC Classification of Madurai Remote Sensing Images with Deep Learning-based Fusion Technique
12.1 Introduction
12.2 Related Work
12.2.1 Image Fusion
12.2.2 Process of Feature Extraction
12.2.3 Feature Selection
12.2.4 Classification of Images
12.3 Problem Statement
12.4 Proposed Work
12.4.1 System Overview
12.4.2 Image Fusion
12.4.3 Feature Extraction
12.4.3.1 Deep Features
12.4.3.1.1 Convolutional Layer
12.4.3.1.2 Pooling Layer
12.4.3.2 Gray Level Co-occurrence Matrix (GLCM)
12.4.3.3 Hu Invariant Moments
12.4.3.4 Color Moments
12.4.4 Feature Selection
12.4.4.1 Ranking Procedure
12.4.4.2 Reconstruction Error (RE) Measure
12.4.5 Image Classification
12.4.5.1 Classification Based on BP Algorithm
12.4.5.2 Classification Based on k-nearest Neighbor
12.4.5.3 Classification Based on Naive Bayes
12.4.5.4 Combined Classifier System (CCS)
12.5 Experimental Results and Discussion
12.5.1 Description of Dataset
12.5.2 Results of the Proposed System
12.5.2.1 Evaluation for Image Fusion
12.5.2.2 Evaluation for Classification
12.5.3 Discussions about the Proposed System
12.6 Conclusion
References
Chapter 13: Human Behavioral Identifiers: A Detailed Discussion
13.1 Introduction to Biometric Technology
13.2 Historical Outline
13.3 The Basic Characteristics
13.3.1 Collectability
13.3.2 Circumvention
13.3.3 Distinctiveness
13.4 Biometric Types
13.4.1 Fingerprints
13.4.2 Photo and Video
13.4.3 Speech
13.4.4 Signature
13.4.5 DNA
13.5 Behavioral Identifiers
13.5.1 Inputting Forms
13.5.2 Physical Movements
13.5.3 Navigation Forms
13.5.4 Engagement Patterns
13.6 Applications
13.6.1 Financial Sector
13.6.2 Security
13.6.3 Mobile Application Domain
13.6.4 Justice, Law and Enforcement Applications
13.6.5 Public Services Applications
13.6.5.1 Healthcare
13.6.5.2 Border Control and Airports
13.6.6 Eye Movement Tracking Applications
13.6.6.1 Aviation
13.6.6.2 Automotive Industry
13.6.6.3 Screen Navigation
13.7 The Rise of Static Biometric Authentication through Physical Characteristics
13.8 Behavioral Biometrics in Today’s Digital World
13.9 Analyzing the Patterns in Human Activity
13.9.1 Physical Movements
13.9.2 Voice Biometrics
13.9.3 Device-based Gestures
13.10 Emerging Technologies in Behavioral Biometrics
13.10.1 Human Behavioral Patterns
13.10.2 Sensors
13.11 Machine Learning/Deep Learning
13.11.1 How it Works
13.12 Behavioral Biometrics Examples
13.12.1 Compromised Credentials
13.12.2 Account Details/Password Sharing
13.12.3 User Substitution
13.12.4 Remote Access Trojans
13.12.5 Insider Threats
13.12.6 USB Rubber Ducky Attacks
13.12.7 Phishing Attacks
13.12.8 Uncertain Attribution
13.12.9 User/Client Carelessness
13.12.10 Identity Fraud
13.12.11 License Mismanagement
13.13 Merits and Demerits
13.14 Future of Behavioral Biometrics
References
Index