Enhancing underwater object detection through water artifact removal and using ensemble transfer learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.

Bibliographische Detailangaben
Hauptverfasser:	Saikat, Nayem Hossain, Jahan, Sarowar, Abrar, Fahim, Rahman, Md. Motaqabbir, Rahman, Md. Ashikur
Weitere Verfasser:	Alam, Md. Golam Rabiul
Format:	Abschlussarbeit
Sprache:	English
Veröffentlicht:	Brac University 2024
Schlagworte:	Domain generalization Object detection Decision tree Water artifact removal Transfer learning Machine learning Cognitive learning theory
Online Zugang:	http://hdl.handle.net/10361/22189

id	10361-22189
record_format	dspace
spelling	10361-221892024-01-21T21:02:54Z Enhancing underwater object detection through water artifact removal and using ensemble transfer learning Saikat, Nayem Hossain Jahan, Sarowar Abrar, Fahim Rahman, Md. Motaqabbir Rahman, Md. Ashikur Alam, Md. Golam Rabiul Rahman, Md Khalilur Department of Computer Science and Engineering, Brac University Domain generalization Object detection Decision tree Water artifact removal Transfer learning Machine learning Cognitive learning theory This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 45-46). The utilization and exploration of deep-sea resources has made underwater autonomous operation increasingly important to mitigate the dangers of the highpressure deep-sea environment. Intelligent computer vision plays a crucial role in underwater autonomous operation, and pre-processing procedures such as weak illumination and low-quality image enhancement are necessary for underwater vision. Underwater object detection plays a critical role in various domains such as marine biology, environmental monitoring, and underwater robotics. However, it is a challenging task due to the complexities of the underwater environment, including poor visibility, light attenuation, and color distortion. In this research paper, we propose a comprehensive methodology for underwater object detection using transfer learning with PyTorch and Jetson Inference. The contributions of this research paper include advancements in underwater object detection through the combination of transfer learning, fine-tuning, and optimization techniques. The utilization of PyTorch and Jetson Inference frameworks provides a powerful and efficient platform for implementing and deploying the model. Additionally, the incorporation of image-clearing techniques ensures the quality of the dataset and improves the model’s performance in challenging underwater conditions. The results of this research have practical implications for a variety of underwater applications, including marine environment monitoring, underwater exploration, and underwater autonomous robots for visual data collection in complex scenarios. By accurately detecting and classifying underwater objects, our methodology contributes to the understanding and preservation of underwater ecosystems, enhancing the capabilities of underwater systems and facilitating decision-making processes. Future work in this field may involve exploring different architectures, incorporating additional data augmentation techniques, and further fine-tuning the model with larger and more diverse underwater datasets. These efforts will contribute to advancing the state-of-the-art in underwater object detection, enabling more robust and efficient solutions for a wide range of underwater applications. . Nayem Hossain Saikat Sarowar Jahan Fahim Abrar Md. Motaqabbir Rahman Md. Ashikur Rahman B.Sc. in Computer Science 2024-01-21T06:49:28Z 2024-01-21T06:49:28Z 2023 2023-05 Thesis ID 17301113 ID 18101712 ID 18101296 ID 17201131 ID 18101608 http://hdl.handle.net/10361/22189 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 46 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Domain generalization Object detection Decision tree Water artifact removal Transfer learning Machine learning Cognitive learning theory
spellingShingle	Domain generalization Object detection Decision tree Water artifact removal Transfer learning Machine learning Cognitive learning theory Saikat, Nayem Hossain Jahan, Sarowar Abrar, Fahim Rahman, Md. Motaqabbir Rahman, Md. Ashikur Enhancing underwater object detection through water artifact removal and using ensemble transfer learning
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
author2	Alam, Md. Golam Rabiul
author_facet	Alam, Md. Golam Rabiul Saikat, Nayem Hossain Jahan, Sarowar Abrar, Fahim Rahman, Md. Motaqabbir Rahman, Md. Ashikur
format	Thesis
author	Saikat, Nayem Hossain Jahan, Sarowar Abrar, Fahim Rahman, Md. Motaqabbir Rahman, Md. Ashikur
author_sort	Saikat, Nayem Hossain
title	Enhancing underwater object detection through water artifact removal and using ensemble transfer learning
title_short	Enhancing underwater object detection through water artifact removal and using ensemble transfer learning
title_full	Enhancing underwater object detection through water artifact removal and using ensemble transfer learning
title_fullStr	Enhancing underwater object detection through water artifact removal and using ensemble transfer learning
title_full_unstemmed	Enhancing underwater object detection through water artifact removal and using ensemble transfer learning
title_sort	enhancing underwater object detection through water artifact removal and using ensemble transfer learning
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22189
work_keys_str_mv	AT saikatnayemhossain enhancingunderwaterobjectdetectionthroughwaterartifactremovalandusingensembletransferlearning AT jahansarowar enhancingunderwaterobjectdetectionthroughwaterartifactremovalandusingensembletransferlearning AT abrarfahim enhancingunderwaterobjectdetectionthroughwaterartifactremovalandusingensembletransferlearning AT rahmanmdmotaqabbir enhancingunderwaterobjectdetectionthroughwaterartifactremovalandusingensembletransferlearning AT rahmanmdashikur enhancingunderwaterobjectdetectionthroughwaterartifactremovalandusingensembletransferlearning
_version_	1814307219325321216

Enhancing underwater object detection through water artifact removal and using ensemble transfer learning

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