基於深度學習的魚類影像分割和辨識

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姓名

陳履軒(Lu-Hsuan Chen) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於深度學習的魚類影像分割和辨識
(Fish Image Segmentation and Classification System Design Based on Deep Learning)

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至系統瀏覽論文 (2024-7-12以後開放)

摘要(中)

魚類在我們的日常生活佔了很大一部分，但是在沒有專業的訓練之下，我們很難去
辨別魚的種類。因此，我們需要研發一個魚類影像分割和辨識系統來幫助一般人辨別一
個圖片中可見的每隻魚的種類。不過從頭研發一個上述的系統相當的消耗人力和時間。
本研究提出一個基於深度學習的魚類影像切割和辨識的系統設計，使用 MIAT 系統設計
方法論，按照 IDEF0 進行模組和階層式系統設計，並使用 GRAFCET 進行離散事件建
模，以進行系統軟體高階合成來建構我們的系統。我們亦展示我們使用的影像標註工具
以及標註流程和規則。本研究以一個魚類圖片資料庫來驗證此一方法論建構出的系統。
實驗結果顯示，本系統可以在驗證資料集上面達到 85%的 top-1 準確度，並在兩個月之
內把本系統研發完成。

摘要(英)

Fish plays an important role in our life, but we can hardly to recognize the type of fish
without professional training. As a result, we would like to design a system which can segment
the part of fish from an image then classify the kind of each part of segments for ordinary
human being. Nevertheless, designing and implementing such system from scratch costs a lot
of human labor and time.

This dissertation proposes a fish image segmentation and classification system design
with the help of deep learning. We adopt the core concepts of MIAT Methodology to construct
the system with IDEF0 for modular and hierarchical system design and GRAFCET of discrete
event modeling. Also, we demonstrate the image annotation tool we use on labeling dataset
and state the protocols of image annotation. We adopt a fish image dataset to verify the system
created with applying MIAT Methodology within two months, and the system shows a top-1
accuracy of 85%.

關鍵字(中)

★ 深度學習
★ 機器學習
★ 影像分割
★ 影像切割
★ 影像辨識

關鍵字(英)

★ deep learning
★ Mask R-CNN
★ ResNet
★ machine learning
★ image segmentation
★ semantic segmentation
★ instance segmentation
★ image classification
★ image recognition

論文目次

摘要 i
Abstract ii
List of Contents v
List of Figures vii
List of Tables ix
Chapter 1. Introduction 1
1.1. Background 1
1.2. Objective 2
1.3. Structure of This Thesis 3
Chapter 2. Review of Image Segmentation and Classification Methods Using Deep Convolutional Neural Networks 4
2.1. Deep Learning-based image segmentation 4
2.1.1. Region-based Semantic Segmentation 5
2.1.2. Fully Convolutional Network-based Semantic Segmentation 9
2.1.3. Weakly Supervised Semantic Segmentation 10
2.2. Image Classification with the Help of Deep Convolutional Neural Networks 10
2.3. Mask R-CNN 13
2.4. ResNet 14
2.5. Brief Summary 15
Chapter 3. System Architecture 18
3.1. MIAT Methodology 18
3.1.1. IDEF0 for Hierarchical and Modular Design of System 19
3.1.2. GRAFCET for Discrete Event System Modeling 21
3.2. The Architecture of Proposed System 22
3.2.1. Segmentation Model 23
3.2.2. Classification Model 23
3.2.3. IDEF0 of the Architecture 24
3.2.4. GRAFCET of the Proposed System 25
3.3. Brief Summary 27
Chapter 4. Fish Image Dataset Architecture and Annotation 28
4.1. Architecture of Fish Image Dataset 28
4.1.1. Dataset of Instance Segmentation Module 28
4.1.2. Dataset Used for Training of Fish Kind Classification 29
4.2. Countering Overfitting Problem 34
4.3. Introduction of VGG Image Annotator 36
4.4. Using VIA to Annotate Single Image 37
4.5. Annotation Protocols of Our Fish Dataset 39
Chapter 5. Experiments 42
5.1. Development Environment 42
5.2. Structure of Segmentation Part 42
5.3. Structure of Classification Part 43
5.4. Benchmark and Experiment Results of Each Module 45
5.5. Brief Summary 54
Chapter 6. Conclusion and Future Prospects 55
6.1. Conclusion 55
6.2. Future Prospects 56
References 58

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指導教授

陳慶瀚

審核日期

2019-8-22

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