https://scholars.lib.ntu.edu.tw/handle/123456789/520871
標題: | Identifying the species of harvested tuna and billfish using deep convolutional neural networks | 作者: | Lu, YC Tung, C Kuo, YF YAN-FU KUO |
關鍵字: | convolutional neural network; deep learning; fish species identification; fishery management; model visualization; transfer learning | 公開日期: | 2020 | 出版社: | OXFORD UNIV PRESS | 卷: | 77 | 期: | 4 | 起(迄)頁: | 1318 | 來源出版物: | ICES JOURNAL OF MARINE SCIENCE | 摘要: | Fish catch species provide essential information for marine resource management. Some international organizations demand fishing vessels to report the species statistics of fish catch. Conventionally, the statistics are recorded manually by observers or fishermen. The accuracy of these statistics is, however, questionable due to the possibility of underreporting or misreporting. This paper proposes to automatically identify the species of common tuna and billfish using machine vision. The species include albacore (Thunnus alalunga), bigeye tuna (Thunnus obesus), yellowfin tuna (Thunnus albacares), blue marlin (Makaira nigricans), Indo-pacific sailfish (Istiophorus platypterus), and swordfish (Xiphias gladius). In this approach, the images of fish catch are acquired on the decks of fishing vessels. Deep convolutional neural network models are then developed to identify the species from the images. The proposed approach achieves an accuracy of at least 96.24%. © International Council for the Exploration of the Sea 2019. All rights reserved. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/520871 | ISSN: | 1054-3139 | DOI: | 10.1093/icesjms/fsz089 | SDG/關鍵字: | artificial neural network; finfish; fish; fishery management; fishing vessel; harvesting; identification method; marine resource; tuna fishery; Istiophorus platypterus; Makaira nigricans; Scombridae; Thunnus alalunga; Thunnus albacares; Thunnus obesus; Xiphias gladius; Xiphiidae |
顯示於: | 生物環境系統工程學系 |
檔案 | 描述 | 大小 | 格式 | |
---|---|---|---|---|
2019升等_參考著作4_Fish Identification.pdf | 1.53 MB | Adobe PDF | 檢視/開啟 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。