| Item type |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2024-04-24 |
| タイトル |
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|
タイトル |
Length estimation of fish detected as non-occluded using a smartphone application and deep learning method |
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言語 |
en |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
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主題 |
Mask R-CNN; Mobile imaging; Occlusion; Total length; composition in catch; Stock assessment |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 著者 |
柴田, 泰宙
岩原, 由佳
眞名野, 将大
金谷, 彩友美
曽根, 亮太
| en |
Sone, Ryota
Aichi Fisheries Research Institute
|
| ja |
曽根, 亮太
愛知県水産試験場
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| ja-Kana |
ソネ, リョウタ
|
Search repository
田村, 怜子
| en |
Tamura, Satoko
Fisheries Technology Center Sagami Bay Experiment Station of Kanagawa Prefectural Government
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| ja |
田村, 怜子
神奈川県水産技術センター相模湾試験場
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| ja-Kana |
タムラ, サトコ
|
Search repository
角田, 直哉
| en |
Kakuta, Naoya
Fisheries Technology Center Sagami Bay Experiment Station of Kanagawa Prefectural Government
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| ja |
角田, 直哉
神奈川県水産技術センター相模湾試験場
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| ja-Kana |
カクタ, ナオヤ
|
Search repository
西野, 智也
| en |
Nishino, Tomoya
Computermind Corp.
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| ja |
西野, 智也
株式会社コンピュータマインド
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| ja-Kana |
ニシノ, トモヤ
|
Search repository
石原, 翔
| en |
Ishihara, Akira
Computermind Corp.
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| ja |
石原, 翔
株式会社コンピュータマインド
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| ja-Kana |
イシハラ, アキラ
|
Search repository
久貝, 俊悟
| en |
Kugai, Shungo
Computermind Corp.
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| ja |
久貝, 俊悟
株式会社コンピュータマインド
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| ja-Kana |
クガイ, シュンゴ
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Search repository
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Uncertainty in stock assessment can be reduced if accurate and precise length composition of catch is available. Length data are usually manually collected, although this method is costly and time-consuming. Recently, some studies have estimated fish species and length from images using deep learning by installing camera systems in fishing vessels or a fish auction center (Álvarez -Ellacuria et al., 2020; Lekunberri et al., 2022; Ovalle et al., 2022; Palmer et al., 2022). Once a deep learning model is properly trained, it does not require expensive and time-consuming manual labor. However, several studies on the deep learning models had monitoring fishing practices using electronic monitoring systems; therefore, it is necessary to solve many issues, such as counting the total number of fish in the catch. In this study, we proposed a new deep learning-based method to estimate fish length using images. Species identification was not performed by the model, and images were taken manually by the measurers; however, length composition was obtained only for non-occluded fish detected by the model. A smartphone application was developed to calculate scale information (cm/pixel) from a known size fish box in fish images, and the Mask R-CNN (Region-based convolutional neural networks) model was trained using 76,161 fish to predict non-occluded fish. Two experiments were conducted to confirm whether the proposed method resulted in errors in the length composition. First, we manually measured the total length (TL) for four species and one genus (categories), estimated the TL using a deep learning method, and calculated the bias. Second, multiple fish in a fish box were photographed simultaneously, and the relative difference between the mean TL estimated from the non-occluded fish and the true mean TL from all fish was calculated. The results showed that the biases of all five categories were from −0.69 cm to 0.37 cm and the range of difference was from −1.14 % to 1.40 % regardless of the number of fish in the fish box. The deep learning method was used not to replace the measurer but to increase their measurement efficiency. The proposed method is expected to increase opportunities for the application of deep learning-based fish length estimation in areas of research that are different from the scope of conventional electronic monitoring systems. |
|
言語 |
en |
| 書誌情報 |
en : Fisheries Research
巻 273,
p. 106970,
発行日 2024-05
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| 出版者 |
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|
出版者 |
Elsevier |
|
言語 |
en |
| ISSN |
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収録物識別子タイプ |
PISSN |
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収録物識別子 |
0165-7836 |
| 書誌レコードID |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
1872-6763 |
| DOI |
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関連タイプ |
isIdenticalTo |
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|
識別子タイプ |
DOI |
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|
関連識別子 |
10.1016/j.fishres.2024.106970 |
| 情報源 |
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関連タイプ |
isIdenticalTo |
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識別子タイプ |
Local |
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関連識別子 |
23237004 |
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言語 |
ja |
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関連名称 |
水産資源研究所 水産資源研究センター 漁業情報解析部（横浜） |
| 関連サイト |
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関連タイプ |
isIdenticalTo |
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識別子タイプ |
URI |
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関連識別子 |
https://www.sciencedirect.com/science/article/pii/S0165783624000341 |
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言語 |
en |
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関連名称 |
Elsevier : Fisheries Research |
| 著者版フラグ |
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出版タイプ |
SMUR |
|
出版タイプResource |
http://purl.org/coar/version/c_71e4c1898caa6e32 |
shibata et al Length estimation of fish detected.pdf (2.5 MB)
