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Introduction

Image segmentation is a crucial task in the field of autonomous driving, where the goal is to partition an image into multiple segments to extract meaningful information for navigation and object detection. With the advancements in computer vision and deep learning technologies, image segmentation has become an essential component in autonomous driving systems to enable vehicles to perceive their surroundings accurately and make informed decisions.

In this thesis, we aim to explore the various techniques and methodologies used for image segmentation in autonomous driving applications. We will also investigate the challenges and limitations faced in this field and propose innovative solutions to overcome them. The research findings will contribute to the development of more reliable and efficient autonomous driving systems that can operate safely in complex environments.

Table of Contents

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of Image Segmentation in Autonomous Driving
2.2 Traditional Image Segmentation Methods
2.3 Deep Learning Approaches for Image Segmentation
2.4 Evaluation Metrics for Image Segmentation
2.5 Challenges in Image Segmentation for Autonomous Driving
2.6 Applications of Image Segmentation in Autonomous Driving
2.7 State-of-the-Art Research in Image Segmentation for Autonomous Driving
2.8 Comparative Analysis of Different Segmentation Techniques
2.9 Future Trends in Image Segmentation for Autonomous Driving
2.10 Summary

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Image Preprocessing Techniques
3.4 Segmentation Algorithms Selection
3.5 Model Training and Evaluation
3.6 Performance Metrics
3.7 Experimental Setup
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Performance Evaluation of Different Segmentation Techniques
4.2 Comparative Analysis of Experimental Results
4.3 Interpretation of Findings
4.4 Implications for Autonomous Driving Systems
4.5 Future Research Directions

Chapter 5: Conclusion and Summary
5.1 Summary of Research Findings
5.2 Contributions to the Field
5.3 Recommendations for Future Work
5.4 Conclusion

Thesis Overview

The advancement of autonomous driving technology relies heavily on the accurate perception of the vehicle’s surroundings, which is made possible through image segmentation techniques. This thesis aims to review the existing literature on image segmentation in autonomous driving, identify the limitations, and propose innovative solutions to improve the performance of segmentation algorithms in challenging environments.

The literature review will provide an overview of traditional and deep learning-based segmentation methods, evaluate their performance using relevant metrics, and discuss the challenges faced in real-world applications. The research methodology will outline the experimental setup, data collection, preprocessing techniques, segmentation algorithms, model training, and evaluation metrics used in this study.

The discussion of findings will present the performance evaluation of different segmentation techniques, comparative analysis of experimental results, interpretation of findings, implications for autonomous driving systems, and future research directions. The conclusion and summary will summarize the research findings, highlight the contributions to the field, provide recommendations for future work, and conclude the thesis on image segmentation for autonomous driving.

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