Design and Implementation of a Real-time Object Detection System using Deep Learning for Autonomous Vehicles

Thesis Abstract

**Abstract
** This thesis presents the design and implementation of a real-time object detection system using deep learning for autonomous vehicles. The advancement of autonomous vehicles relies heavily on the ability to accurately detect and recognize objects in their surroundings in real-time. Deep learning techniques, particularly convolutional neural networks (CNNs), have shown remarkable success in various computer vision tasks, including object detection. This project focuses on developing a robust object detection system that can operate efficiently in real-time scenarios to enhance the safety and performance of autonomous vehicles. The thesis begins with an introduction that outlines the background of the study, the problem statement, research objectives, limitations, scope, significance of the study, structure of the thesis, and definitions of key terms. A comprehensive literature review in Chapter Two explores existing research on deep learning-based object detection systems, CNN architectures, object detection algorithms, and their applications in the field of autonomous vehicles. Chapter Three details the research methodology, including data collection, preprocessing, model training, evaluation metrics, and experimental setup. The methodology section also covers the selection of deep learning frameworks, dataset annotation techniques, and the fine-tuning process for optimizing the object detection model. Chapter Four presents a detailed discussion of the findings obtained from the experiments conducted in this study. It analyzes the performance of the developed object detection system in terms of accuracy, speed, and robustness under varying environmental conditions. The chapter also discusses the challenges encountered during the implementation phase and proposes potential solutions for further improvement. Finally, Chapter Five concludes the thesis by summarizing the key findings, highlighting the contributions of the study, and discussing the implications of the developed real-time object detection system for autonomous vehicles. The conclusion also outlines future research directions and potential areas for further exploration to enhance the object detection capabilities of autonomous vehicles using deep learning techniques. Overall, this thesis contributes to the advancement of autonomous vehicle technology by presenting a novel approach to real-time object detection using deep learning. The developed system demonstrates promising results in accurately detecting and recognizing objects in dynamic environments, paving the way for safer and more efficient autonomous driving systems.