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  1. W100 学外関連団体
  2. 中部森林学会
  3. 中部森林研究
  4. 68

深層学習に基づく野生動物認識システム

https://doi.org/10.18999/chufr.68.25
124991ee-e4e7-4a1f-8bc7-dd4bc71a25fd
名前 / ファイル ライセンス アクション
chufr_68_25.pdf chufr_68_25.pdf (1.2 MB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2020-10-05
タイトル
タイトル 深層学習に基づく野生動物認識システム
その他のタイトル
その他のタイトル A wildlife recognition system based on deep learning
著者 上原, 祐介

× 上原, 祐介

WEKO 101431

上原, 祐介

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大谷, 直也

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WEKO 101432

大谷, 直也

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西本, 圭佑

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WEKO 101433

西本, 圭佑

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川﨑, 邦将

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WEKO 101434

川﨑, 邦将

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松井, 康浩

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松井, 康浩

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長田, 茂美

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WEKO 101436

長田, 茂美

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北村, 俊平

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北村, 俊平

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矢田, 豊

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矢田, 豊

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UEHARA, Yusuke

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UEHARA, Yusuke

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OTANI, Naoya

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WEKO 101440

OTANI, Naoya

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NISHIMOTO, Keisuke

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WEKO 101441

NISHIMOTO, Keisuke

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KAWASAKI, Kunimasa

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WEKO 101442

KAWASAKI, Kunimasa

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MATSUI, Yasuhiro

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WEKO 101443

MATSUI, Yasuhiro

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NAGATA, Shigemi

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NAGATA, Shigemi

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KITAMURA, Shumpei

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WEKO 101445

KITAMURA, Shumpei

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YADA, Yutaka

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YADA, Yutaka

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キーワード
主題Scheme Other
主題 野生動物認識
キーワード
主題Scheme Other
主題 カメラトラップ
キーワード
主題Scheme Other
主題 深層学習
キーワード
主題Scheme Other
主題 画像認識
キーワード
主題Scheme Other
主題 セマンティックセグメンテーション
抄録
内容記述 野生動物の保護管理への対策を講じるためには,野生動物の生息状況の調査が必須である。このため,カメラトラップを用いた調査が行われているが,大量に撮影される画像を調査者が確認して,野生動物を見つけ出し,種を同定する労力の大きさが間題となっている。この間題を解決するため,深層学習に基づいて,画像から野生動物の在/不在や種を認識し,画像上の領域とともに提示することで,調査者の労力を軽減する野生動物認識システムを構築した。石川県農林総合研究センター林業試験場で撮影した画像を用いて本システムを評価した結果,野生動物の種の同定作業の6割以上の労力が削減可能であり,調査者の労力軽減に有用であることが示された。
内容記述タイプ Abstract
出版者
出版者 中部森林学会事務局
言語
言語 jpn
資源タイプ
資源タイプresource http://purl.org/coar/resource_type/c_6501
タイプ journal article
ID登録
ID登録 10.18999/chufr.68.25
ID登録タイプ JaLC
ISSN(print)
収録物識別子タイプ ISSN
収録物識別子 1342-971X
書誌情報 中部森林研究

巻 68, p. 25-30, 発行日 2020-05-30
著者版フラグ
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