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Introduction

The advancement of deep learning technology has revolutionized various industries, including autonomous vehicles. Image segmentation is a crucial aspect of autonomous driving systems as it enables the vehicles to accurately identify and understand the surrounding environment. Deep learning techniques have shown promising results in image segmentation tasks, making them an ideal choice for autonomous vehicle applications. This thesis aims to investigate the use of deep learning algorithms for image segmentation in autonomous vehicles and explore their potential benefits and limitations.

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
– Overview of autonomous vehicles
– Image segmentation techniques
– Deep learning in autonomous driving
– Challenges in image segmentation for autonomous vehicles

Chapter 3: Research Methodology
– Data collection and preprocessing
– Deep learning model selection
– Training and testing process
– Evaluation metrics
– Parameter tuning
– Experiment design
– Data analysis techniques
– Ethics considerations

Chapter 4: Discussion of Findings
– Performance evaluation of deep learning models
– Comparison with traditional image segmentation methods
– Impact of different parameters on segmentation accuracy
– Challenges encountered during the research
– Potential improvements and future research directions

Chapter 5: Conclusion and Summary
– Summary of key findings
– Contributions to the field
– Implications for autonomous vehicle technology
– Recommendations for future research

Thesis Overview
Image segmentation is a critical task in the field of autonomous vehicles, as it plays a crucial role in enabling vehicles to understand and interpret their surroundings. Deep learning algorithms have shown great potential in image segmentation tasks, offering improved accuracy and efficiency compared to traditional methods. This thesis aims to explore the use of deep learning techniques for image segmentation in autonomous vehicles and evaluate their performance in real-world scenarios.

The literature review will provide an overview of the current state-of-the-art in autonomous vehicles, image segmentation techniques, and deep learning applications in the field. The research methodology will outline the data collection process, model selection, training techniques, evaluation metrics, and ethical considerations. The discussion of findings will present the results of the experiments conducted, including the performance evaluation of deep learning models, comparison with traditional methods, and challenges faced during the research.

The conclusion and summary will highlight the key findings of the study, contributions to the field, implications for autonomous vehicle technology, and recommendations for future research. Overall, this thesis aims to contribute to the advancement of image segmentation techniques for autonomous vehicles using deep learning, ultimately improving the safety and efficiency of autonomous driving systems.

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