| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-03-31 |
| タイトル |
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タイトル |
Revolutionizing Cow Welfare Monitoring: A Novel Top-View Perspective with Depth Camera-Based Lameness Classification |
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言語 |
en |
| 言語 |
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言語 |
eng |
| キーワード |
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言語 |
en |
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キーワード |
decision tree (DT) |
| キーワード |
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言語 |
en |
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キーワード |
depth sensing camera |
| キーワード |
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言語 |
en |
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キーワード |
detection and tracking |
| キーワード |
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言語 |
en |
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キーワード |
k-nearest neighbor (KNN) |
| キーワード |
|
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言語 |
en |
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キーワード |
lameness |
| キーワード |
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言語 |
en |
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キーワード |
random forest (RF) |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Tun, San Chain
Onizuka, Tsubasa
Tin, Pyke
相川, 勝
WEKO
12201
e-Rad_Researcher
20976641
| ja |
相川, 勝
宮崎大学
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| ja-Kana |
アイカワ, マサル
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| en |
Aikawa, Masaru
University of Miyazaki
|
Search repository
小林, 郁雄
WEKO
5214
e-Rad_Researcher
20576293
| ja |
小林, 郁雄
宮崎大学
|
| ja-Kana |
コバヤシ, イクオ
|
| en |
Kobayashi, Ikuo
University of Miyazaki
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Search repository
ティ ティ ズイン
WEKO
31575
e-Rad_Researcher
30536959
| ja |
ティ ティ ズイン
宮崎大学
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| ja-Kana |
ティ ティ ズイン
|
| en |
Thi Thi Zin
University of Miyazaki
|
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 |
This study innovates livestock health management, utilizing a top-view depth camera for accurate cow lameness detection, classification, and precise segmentation through integration with a 3D depth camera and deep learning, distinguishing it from 2D systems. It underscores the importance of early lameness detection in cattle and focuses on extracting depth data from the cow’s body, with a specific emphasis on the back region’s maximum value. Precise cow detection and tracking are achieved through the Detectron2 framework and Intersection Over Union (IOU) techniques. Across a three-day testing period, with observations conducted twice daily with varying cow populations (ranging from 56 to 64 cows per day), the study consistently achieves an impressive average detection accuracy of 99.94%. Tracking accuracy remains at 99.92% over the same observation period. Subsequently, the research extracts the cow’s depth region using binary mask images derived from detection results and original depth images. Feature extraction generates a feature vector based on maximum height measurements from the cow’s backbone area. This feature vector is utilized for classification, evaluating three classifiers: Random Forest (RF), K-Nearest Neighbor (KNN), and Decision Tree (DT). The study highlights the potential of top-view depth video cameras for accurate cow lameness detection and classification, with significant implications for livestock health management. |
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言語 |
en |
| 内容記述 |
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内容記述タイプ |
Other |
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内容記述 |
Citation: Tun, S.C.; Onizuka, T.; Tin, P.; Aikawa, M.; Kobayashi, I.; Zin, T.T. Revolutionizing Cow Welfare Monitoring: A Novel Top-View Perspective with Depth Camera-Based Lameness Classification. J. Imaging 2024, 10, 67. https://doi.org/10.3390/jimaging10030067 |
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言語 |
en |
| bibliographic_information |
en : Journal of Imaging
巻 10,
号 3,
p. 67,
発行日 2024-03-08
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| 出版者 |
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出版者 |
MDPI AG |
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言語 |
en |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2313-433X |
| item_10001_relation_14 |
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関連タイプ |
isVersionOf |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.3390/jimaging10030067 |
| 権利 |
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権利情報 |
10.3390/jimaging10030067 |
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言語 |
en |
| 出版タイプ |
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出版タイプ |
VoR |
Fulltext (7.4 MB)
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