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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 郭彥甫 | |
| dc.contributor.author | Yi-Chin Lu | en |
| dc.contributor.author | 呂易晉 | zh_TW |
| dc.date.accessioned | 2021-06-17T08:14:49Z | - |
| dc.date.available | 2024-08-20 | |
| dc.date.copyright | 2019-08-20 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-14 | |
| dc.identifier.citation | Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford university press.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73954 | - |
| dc.description.abstract | 漁獲統計資料是海洋資源管理中必不可少的資訊，而統計資料通常是由海上觀察員或船員手動紀錄的，但這樣人工紀錄的方式非常耗時且極具主觀性，因此存在自動化收集與回報漁獲資料的需求。然而漁船甲板上時常充滿各式的雜物，使得自動化的收集與回報漁獲資料充滿挑戰性。近年來由於卷積神經網路 (convolutional neural networks, CNNs) 越來越受歡迎且廣泛被應用於各種複雜的機器視覺任務。因此本研究利用深度卷積神經網路自動辨識11種延繩釣漁船常捕獲之魚種／類，這些魚種／類包含長鰭鮪魚 (Thunnus alalunga)、大目鮪魚 (T. obesus)、黃鰭鮪魚 (T. albacares)、南方黑鮪 (T. maccoyii)、黑皮旗魚 (Makaira nigricans)、雨傘旗魚 (Istiophorus platypterus)、劍旗魚 (Xiphias gladius)與鬼頭刀 (Coryphaena hippurus)。本研究之自動魚種辨識模型使用四種不同的深度卷積神經網路架構，包含：VGG-16、ResNet-50、DenseNet-201與MobileNetV2，搭配center loss function 進行訓練。研究中卷積神經網路模型之準確率最高可達95.83%，而在圖像顯示卡 (Graphics Processing Unit, GPU) 與中央處理器(Central Processing Unit, CPU)最快之運行速度分別可達1.75與107.82毫秒∕影像。 | zh_TW |
| dc.description.abstract | Fish catch statistics reported by vessels are essential information for the management of marine resource. The statistics were conventionally recorded by observers or fishermen. Manual recording is time consuming and can be subjective; thus, there is a demand for automatic statistics collection and reporting. The decks of fishing vessels are usually full of miscellaneous items, making automatic reporting of the statistics challenging. In recent years, convolutional neural networks (CNNs) have become increasingly popular and been applied to solving complex machine vision tasks. This study proposed to automatically identify 11 species or types of fish harvested by longliners using deep CNNs. The species included albacore (Thunnus alalunga), bigeye tuna (T. obesus), yellowfin tuna (T. albacares), southern bluefin tuna (T. maccoyii), blue marlin (Makaira nigricans), Indo-Pacific sailfish (Istiophorus platypterus), swordfish (Xiphias gladius), and dolphin fish (Coryphaena hippurus). Four deep CNNs modified from architectures VGG-16, ResNet-50, DenseNet-201, and MobileNetV2 were trained to identify the species and types of the fish in images collected on longliners. Center loss function was also applied during training for improving the performance of the CNNs. The CNNs reached an accuracy of as high as 95.83% and required a processing time of as short as 1.75 ms using a GPU and 107.82ms using a CPU. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T08:14:49Z (GMT). No. of bitstreams: 1 ntu-108-R06631011-1.pdf: 2004965 bytes, checksum: fc65a59c3aa024c7205751f47077bd94 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | ACKNOWLEDGEMENTS i
摘要 ii ABSTRACT iii TABLE OF CONTENTS iv LIST OF FIGURES vi LIST OF TABLES viii CHAPTER 1. INTRODUCTION 1 1.1 General background information 1 1.2 Objectives 2 1.3 Organization 3 CHAPTER 2. LITERATURE REVIEW 4 2.1 Traditional image-based approaches for fish species identification 4 2.2 Convolutional Neural Network 4 2.3 Fine-grained classification 5 CHAPTER 3. MATERIALS AND METHODS 7 3.1 Fish images collection 7 3.2 Image preprocessing and manipulation 8 3.3 Transfer learning and pre-trained models 9 3.4 Model training details 15 CHAPTER 4. RESULTS AND DISSCUSSION 17 4.1 Convergence of the models 17 4.2 Performance of the trained models 17 4.3 Challenging cases 20 4.4 Processing time of the models 20 4.5 Performance comparison with baseline model 21 CHAPTER 5. CONCLUSIONS 23 REFERENCES 24 | |
| dc.language.iso | en | |
| dc.subject | 中央損失函數 | zh_TW |
| dc.subject | 卷積神經網路 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 細粒度分類 | zh_TW |
| dc.subject | 魚種辨識 | zh_TW |
| dc.subject | Convolutional neural network | en |
| dc.subject | Center loss | en |
| dc.subject | Fish species identification | en |
| dc.subject | Fine-grained classification | en |
| dc.subject | Deep learning | en |
| dc.title | 利用深度卷積神經網路辨識延繩釣漁獲中常見之魚種 | zh_TW |
| dc.title | Identifying Species of Common Sea Fish Harvested by Longliner Using Deep Convolutional Neural Networks | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 謝清祿,花凱龍,鄭文皇 | |
| dc.subject.keyword | 中央損失函數,卷積神經網路,深度學習,細粒度分類,魚種辨識, | zh_TW |
| dc.subject.keyword | Center loss,Convolutional neural network,Deep learning,Fine-grained classification,Fish species identification, | en |
| dc.relation.page | 28 | |
| dc.identifier.doi | 10.6342/NTU201903085 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-08-15 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物產業機電工程學研究所 | zh_TW |
| 顯示於系所單位： | 生物機電工程學系 | |
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