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Introduction

Object detection is a crucial task in computer vision and has numerous applications such as autonomous driving, surveillance, and image analysis. You Only Look Once version 4 (YOLOv4) is one of the most popular and powerful object detection models, known for its efficiency and accuracy. In this thesis, we aim to build an object detection model using YOLOv4 and explore its capabilities and limitations.

1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of the Study
1.5 Limitation of the Study
1.6 Scope of Study
1.7 Significance of the Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Object Detection
2.2 Deep Learning
2.3 Convolutional Neural Networks
2.4 YOLO Algorithm
2.5 YOLOv4
2.6 Comparison with Other Object Detection Models
2.7 Applications of Object Detection
2.8 Challenges in Object Detection
2.9 Transfer Learning in Object Detection
2.10 Evaluation Metrics in Object Detection

Chapter 3: System Design and Methodology
3.1 Data Collection and Preprocessing
3.2 Model Architecture
3.3 Training Process
3.4 Hyperparameter Tuning
3.5 Dataset Augmentation
3.6 Evaluation Metrics
3.7 Fine-tuning and Transfer Learning
3.8 Model Optimization

Chapter 4: System Implementation
4.1 Setting up the Environment
4.2 Data Annotation
4.3 Model Training
4.4 Model Testing
4.5 Performance Evaluation
4.6 Model Deployment
4.7 Real-time Object Detection
4.8 Model Integration and Compatibility

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contribution of the Study
5.3 Future Work
5.4 Conclusion

Thesis Overview on Building an Object Detection Model using YOLOv4

In this thesis, we aim to build an object detection model using You Only Look Once version 4 (YOLOv4), a state-of-the-art deep learning model known for its efficiency and accuracy in real-time object detection tasks. The thesis will be divided into five main chapters focusing on the introduction, literature review, system design and methodology, system implementation, and conclusion.

Chapter 1 provides an introduction to the study, outlining the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms related to object detection using YOLOv4. Chapter 2 presents a comprehensive literature review covering topics such as object detection, deep learning, convolutional neural networks, YOLO algorithm, YOLOv4, comparison with other models, applications, challenges, transfer learning, and evaluation metrics in object detection.

Chapter 3 details the system design and methodology, including data collection, preprocessing, model architecture, training process, hyperparameter tuning, dataset augmentation, evaluation metrics, fine-tuning, and model optimization. Chapter 4 focuses on the system implementation, covering setting up the environment, data annotation, model training, testing, performance evaluation, deployment, real-time detection, and integration.

Chapter 5 concludes the thesis with a summary of findings, contribution of the study, future work, and overall conclusion on the effectiveness and capabilities of building an object detection model using YOLOv4. The thesis aims to provide insights into the process of developing an object detection model using advanced deep learning techniques and contributing to the field of computer vision.

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