Master thesis Computer science: Deep learning-based approach for water crystal classification

Depending on the origin of the water and the formation process, crystals are divided into three main types: snow crystals, ice crystals, and water crystals. From the shape of the crystal, the purity and the texture level are clearly reflected, then it enables us to assess the quality of the water.
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Master thesis Computer science: Deep learning-based approach for water crystal classification VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY DOAN THI HIENDEEP LEARNING-BASED APPROACH FOR WATER CRYSTAL CLASSIFICATION MASTER THESIS Major: Computer Science HA NOI - 2021AbstractAlmost the earth’s surface area is covered by water. As it is pointed out in the 2020edition of the World Water Development Report, climate change challenges the sustain-ability of water resources. It is important to monitor the quality of water to preservesustainable water resources. Quality of water can be related to the water crystal struc-ture, solid-state of water, methods to understand water crystal help to improve waterquality. First step, water crystal exploratory analysis has been initiated under cooper-ation with the Emoto Peace Project (EPP). The 5K EPP Dataset has been created asthe first world-wide small dataset of water crystals. Our research focused on reducinginherent limitations when fitting machine learning models to the 5K EPP dataset. Onemajor result is the classification of water crystals and how to split our small dataset intomost related groups. Using the 5K EPP dataset human observations and past researcheson snow crystal classification, we provided a simple set of visual labels to name watercrystal shapes, with 12 categories. A deep learning-based method has been used to auto-matically do the classification task with a subset of the labeled dataset. The classificationachieved high accuracy when fine-tuning the ResNet pretrained model.Keywords: Water crystal, Deep learning, Fine-tuning, Supervised, Classification. iiiAcknowledgementsI would first like to thank my thesis supervisor Dr. Tran Quoc Long, Head of the Depart-ment of Computer Science at the University of Engineering and Technology. Thanks forhis insightful comments both in my work and in this thesis, for his support, and manymotivating discussions. I also want to acknowledge my co-supervisor Dr. Frederic Andres from the Na-tional Institute of Informatics, Japan for offering me the internship opportunities at NII,Japan and leading me working on diverse exciting projects. Without his support andexperience, I could not achieve today result. Besides, I have been very privileged to get to know and to collaborate with manyother great collaborators. Finally, I must express my very profound gratitude to my family for providing mewith unfailing support and continuous encouragement throughout my years of study andthrough the process of researching and writing this thesis. This accomplishment wouldnot have been possible without them. ivDeclarationI declare that the thesis has been composed by myself and that the work has not besubmitted for any other degree or professional qualification. I confirm that the worksubmitted is my own, except where work which has formed part of jointly-authoredpublications has been included. My contribution and those of the other authors to thiswork have been explicitly indicated below. I confirm that appropriate credit has beengiven within this thesis where reference has been made to the work of others. This study was conceived by all of the authors. I carried out the main idea(s) andimplemented all the model(s) and material(s). I certify that, to the best of my knowledge, my thesis does not infringe upon any-one’s copyright nor violate any proprietary rights and that any ideas, techniques, quota-tions, or any other material from the work of other people included in my thesis, pub-lished or otherwise, are fully acknowledged in accordance with the standard referencingpractices. Furthermore, to the extent that I have included copyrighted material, I certifythat I have obtained a written permission from the copyright owner(s) to include suchmaterial(s) in my thesis and have fully authorship to improve these materials. Master student Doan Thi Hien vTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivDeclaration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viAcronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiiList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Difficulties and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Common Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.5 Contributions and Structure of the Thesis . . . . . . . . . . . . . . . . . . 62 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.1 Manually Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Deep Learning-Based Approaches . . . . . . . . . . . . . . . . . . . . . . 93 The 5K EPP dataset . . . . . . ...