WEKO3
アイテム
Effects of input-image size on performance of fish detection and species classification using deep learning
https://fra.repo.nii.ac.jp/records/2014850
https://fra.repo.nii.ac.jp/records/20148502b700cde-b3d4-4551-a785-937d24e7d5b0
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
25237001.pdf (3.7 MB)
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| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||||||||||||||||||||||
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| 公開日 | 2025-07-17 | |||||||||||||||||||||||||||||
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| タイトル | Effects of input-image size on performance of fish detection and species classification using deep learning | |||||||||||||||||||||||||||||
| 言語 | en | |||||||||||||||||||||||||||||
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| 言語 | eng | |||||||||||||||||||||||||||||
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| 言語 | en | |||||||||||||||||||||||||||||
| 主題Scheme | Other | |||||||||||||||||||||||||||||
| 主題 | Mask R-CNN; Species classification; Input image size; Set-net; Stock assessment | |||||||||||||||||||||||||||||
| 資源タイプ | ||||||||||||||||||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||||||||||||||||
| 資源タイプ | journal article | |||||||||||||||||||||||||||||
| アクセス権 | ||||||||||||||||||||||||||||||
| アクセス権 | open access | |||||||||||||||||||||||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||||||||||||||||||||||
| 著者 |
岩原, 由佳
× 岩原, 由佳
WEKO
76
× 柴田, 泰宙
WEKO
892
× 眞名野, 将大
WEKO
1160
× Nishino, Tomoya
× Kariya, Ryosuke
× Yaemori, Hiroki
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| 内容記述タイプ | Abstract | |||||||||||||||||||||||||||||
| 内容記述 | Deep learning has been extensively used in fisheries science, as it enables the acquisition of information regarding the body length and stock-abundance index of target fish from images, thereby facilitating stock assessment and management. However, generally, multiple species appear together in images obtained from fisheries, necessitating the classification of fish species before extracting relevant biological information. Improving the performance of fish detection and species classification is crucial as it affects the quality of biological information that could be inferred from images. Previous studies have reported that increasing the inputimage size can affect the classification accuracy. Identification characteristics of fish are small in comparison with their body size, and increasing the image size can affect the classification accuracy; however, there are no reports on the effect of image size on fish species-classification accuracy. Herein, different input-image sizes were taken to evaluate the effect of input-image size on the performance of fish detection and species classification. Fish images (41,922 fish across 41 classes) were acquired from conveyor belts to sort set-net fish catches. Fish were detected and classified using a mask region-based convolutional neural network. The effect of input-image size on performance was examined using nine datasets in three image sizes of 1333 × 888, 2000 × 1333, and 2666 × 1777 pixels, obtaining an average mAP50–95 value of 0.586, 0.612, and 0.609, respectively. Larger image sizes offered improved performance compared with that of the smallest, averaging 0.026 and 0.023 improvements in mAP50–95 at two larger image sizes. However, when comparing the degree of improvement between image sizes of 2000 × 1333 pixels and 2666 × 1777 pixels under fine-tuning conditions, the former size resulted in higher performance. Performance was observed to improve for species with low performance at the smallest image size; therefore, we can say that increasing the input-image size is a simple and effective way for improving detection and classification performance for these species. |
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| 言語 | en | |||||||||||||||||||||||||||||
| 書誌情報 |
en : Ecological Informatics 巻 93, p. 103566, 発行日 2026-02 |
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| 関連タイプ | isIdenticalTo | |||||||||||||||||||||||||||||
| 識別子タイプ | DOI | |||||||||||||||||||||||||||||
| 関連識別子 | 10.1016/j.ecoinf.2025.103566 | |||||||||||||||||||||||||||||
| 言語 | en | |||||||||||||||||||||||||||||
| 関連名称 | Elsevier | |||||||||||||||||||||||||||||
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| 関連タイプ | isIdenticalTo | |||||||||||||||||||||||||||||
| 識別子タイプ | Local | |||||||||||||||||||||||||||||
| 関連識別子 | 25237001 | |||||||||||||||||||||||||||||
| 言語 | ja | |||||||||||||||||||||||||||||
| 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 情報企画グループ | |||||||||||||||||||||||||||||
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| 関連タイプ | isIdenticalTo | |||||||||||||||||||||||||||||
| 識別子タイプ | URI | |||||||||||||||||||||||||||||
| 関連識別子 | https://doi.org/10.1016/j.ecoinf.2025.103566 | |||||||||||||||||||||||||||||
| 言語 | en | |||||||||||||||||||||||||||||
| 関連名称 | Elsevier | |||||||||||||||||||||||||||||
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| 出版タイプ | VoR | |||||||||||||||||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||||||||||||||||
