Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/546306
Title: Enhancement and classification of underwater species images using deep learning
Researcher: Dhana Lakshmi M
Guide(s): Sakthivel Murugan S
Keywords: Computer Science
Computer Science Information Systems
Deep Learning
Ecosystem
Engineering and Technology
Marine species
Underwater species images
University: Anna University
Completed Date: 2023
Abstract: Automated classification of Marine species plays a significant role in studies dealing with a species count for population evaluation, behavior analysis, monitoring of the ecosystem, realizing the association between species and ecosystem, etc. Most marine species appear identical to human perception but differ in shape, structure, and color. Extracting those similar features from a degraded species image is challenging for traditional vision techniques. These minute-variation feature scan be extracted efficiently with the help of deep learning techniques. Hence, there is a need for automated techniques that can consistently classify marine species using deep learning in underwater videos without human interaction. This will lead to the production of knowledge on fish species and their habitats. The video and image information of marine species is acquired from underwater survey equipment such as Remotely Operated Vehicle (ROV), Autonomous Underwater Vehicle(AUV), etc. The underwater captured marine species images still suffer from image degradations, leading to the misclassification of species. Hence, the primary objective of this thesis focuses to develop effective image visibility improvement techniques for degraded underwater species images and to develop a robust classification-based network on computational intelligence approaches. To achieve this, the proposed research works were carried out in a series of modules namely: Data Acquisition module, Degraded Image Visibility Improvement module, and Deep learning species classifier to recognize the marine species images. newline
Pagination: xxi, 175p.
URI: http://hdl.handle.net/10603/546306
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File937.18 kBAdobe PDFView/Open
02_prelim pages.pdf3.55 MBAdobe PDFView/Open
03_content.pdf1.15 MBAdobe PDFView/Open
04_abstract.pdf981.2 kBAdobe PDFView/Open
05_chapter1.pdf1.57 MBAdobe PDFView/Open
06_chapter2.pdf1.07 MBAdobe PDFView/Open
07_chapter3.pdf1.74 MBAdobe PDFView/Open
08_chapter4.pdf2.79 MBAdobe PDFView/Open
09_chapter5.pdf2.61 MBAdobe PDFView/Open
10_annexures.pdf422.5 kBAdobe PDFView/Open
80_recommendation.pdf249.17 kBAdobe PDFView/Open
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