https://scholars.lib.ntu.edu.tw/handle/123456789/430859
標題: | Identifying species of common sea fish harvested by longliner using deep convolutional neural networks | 作者: | Lu, Yi Chin YAN-FU KUO |
關鍵字: | Convolutional neural network | Deep learning | Fine-grained classification | Fish species identification | Model visualization;Convolutional neural network; Deep learning; Fine-grained classification; Fish species identification; Model visualization | 公開日期: | 1-一月-2019 | 來源出版物: | 2019 ASABE Annual International Meeting | 摘要: | © 2019 ASABE Annual International Meeting. All rights reserved. Fish catch statistics reported by vessels are essential information for the management of marine resource. The statistics, including species, body lengths, harvest time, and harvest location, were conventionally recorded by observers or fishermen. Manual recording is time consuming and can be subjective; thus, the accuracy of the statistics is questionable. 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, VGG-16, ResNet-50, DenseNet-201, MobileNetV2, were modified to identify the species of the fish. The CNNs outperformed conventional machine learning approaches and reached an accuracy of as high as 95.82% in species identification. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/430859 | DOI: | 10.13031/aim.201900399 | SDG/關鍵字: | Convolution; Deep learning; Fish; Fishing vessels; Harvesting; Information management; Marine biology; Neural networks; Conventional machines; Convolutional neural network; Coryphaena hippurus; Fine grained; Fish species; Model visualization; Southern bluefin tuna; Species identification; Deep neural networks |
顯示於: | 生物機電工程學系 |
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