Towards devising an effective and reliable means of fish detection and classification through the exploration of various deep learning algorithms

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.

Bibliographic Details
Main Authors:	Farhan, Rafid, Rahman, Ninad Abdur, Ahsan, Syeda Sara Ummy
Other Authors:	Rodoshi, Ahanaf Hassan
Format:	Thesis
Language:	English
Published:	Brac University 2024
Subjects:	Fish detection CNN model for classification YOLOv4 for detection Artificial Intelligence (AI) VGG-16 DenseNet Xception Artificial intelligence Neural networks (Computer science)
Online Access:	http://hdl.handle.net/10361/23055

id	10361-23055
record_format	dspace
spelling	10361-230552024-06-02T21:02:18Z Towards devising an effective and reliable means of fish detection and classification through the exploration of various deep learning algorithms Farhan, Rafid Rahman, Ninad Abdur Ahsan, Syeda Sara Ummy Rodoshi, Ahanaf Hassan Khondaker, Arnisha Department of Computer Science and Engineering, Brac University Fish detection CNN model for classification YOLOv4 for detection Artificial Intelligence (AI) VGG-16 DenseNet Xception Artificial intelligence Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 42-43). Due to a number of reasons, marine ecosystems change with certain species of fish disappearing while novel species of fishes become a new staple within a given ecosystem, e.g., a lake, river, etc. Monitoring these changes in ecosystems as different species dwindle and swell in number is crucial for marine researchers, fishery owners, and fish species preservation programs. These increase and decrease in numbers indicate changes in environmental conditions that either favours a certain species or does not. In order to study these changes in conditions, it is imperative to firstly detect the changes in the population of species which is where we come in. The challenges for an underwater project range from water pressure, lack of sunlight, different orientations of fish, the motion of aquatic plants, riverbed structures, and the sheer diversity of shapes in different species. Machine learning and image processing technologies can be of significant importance in identifying such underwater fish species. In our research, we decided to use Convolutional Neural Networks (CNN), namely YOLOv4, to detect fish in input image frames. To classify the fish species, we will use a CNN network. The fusion of these networks is proposed in order to achieve a high level of classification accuracy of fish species from smallsized samples. In order to demonstrate the effectiveness of the model, we propose two datasets, namely BDIndigeneousFish and A-Large-Scale-Fish-Dataset is used, which contain a vast range of image data of several species from different habitats. The image data is fed into the Darknet, which identifies and detects the fish pixels in the image frame. Furthermore, these input images are then passed on to CNN for classification. Rafid Farhan Ninad Abdur Rahman Syeda Sara Ummy Ahsan B.Sc. in Computer Science 2024-06-02T07:24:59Z 2024-06-02T07:24:59Z 2022 2022-01 Thesis ID 18101231 ID 18101223 ID 18101437 http://hdl.handle.net/10361/23055 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. 43 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Fish detection CNN model for classification YOLOv4 for detection Artificial Intelligence (AI) VGG-16 DenseNet Xception Artificial intelligence Neural networks (Computer science)
spellingShingle	Fish detection CNN model for classification YOLOv4 for detection Artificial Intelligence (AI) VGG-16 DenseNet Xception Artificial intelligence Neural networks (Computer science) Farhan, Rafid Rahman, Ninad Abdur Ahsan, Syeda Sara Ummy Towards devising an effective and reliable means of fish detection and classification through the exploration of various deep learning algorithms
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
author2	Rodoshi, Ahanaf Hassan
author_facet	Rodoshi, Ahanaf Hassan Farhan, Rafid Rahman, Ninad Abdur Ahsan, Syeda Sara Ummy
format	Thesis
author	Farhan, Rafid Rahman, Ninad Abdur Ahsan, Syeda Sara Ummy
author_sort	Farhan, Rafid
title	Towards devising an effective and reliable means of fish detection and classification through the exploration of various deep learning algorithms
title_short	Towards devising an effective and reliable means of fish detection and classification through the exploration of various deep learning algorithms
title_full	Towards devising an effective and reliable means of fish detection and classification through the exploration of various deep learning algorithms
title_fullStr	Towards devising an effective and reliable means of fish detection and classification through the exploration of various deep learning algorithms
title_full_unstemmed	Towards devising an effective and reliable means of fish detection and classification through the exploration of various deep learning algorithms
title_sort	towards devising an effective and reliable means of fish detection and classification through the exploration of various deep learning algorithms
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/23055
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_version_	1814308270475575296

Towards devising an effective and reliable means of fish detection and classification through the exploration of various deep learning algorithms

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