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Breathing Pattern Analysis in Cattle Using Infrared Thermography and Computer Vision
http://hdl.handle.net/10458/0002000307
http://hdl.handle.net/10458/00020003075de37681-a0f8-4c92-8403-4e5fd5332c8b
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
本文 (3.2 MB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||
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| 公開日 | 2023-11-07 | |||||
| タイトル | ||||||
| タイトル | Breathing Pattern Analysis in Cattle Using Infrared Thermography and Computer Vision | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| キーワード | breathing pattern | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| キーワード | infrared thermography | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| キーワード | cattle health and welfare | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| キーワード | computer vision | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| キーワード | machine learning | |||||
| 資源タイプ | ||||||
| 資源タイプ | journal article | |||||
| 著者 |
Kim, Sueun
× Kim, Sueun× 日高, 勇一 |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Breathing patterns can be considered a vital sign providing health information. Infrared thermography is used to evaluate breathing patterns because it is non-invasive. Our study used not only sequence temperature data but also RGB images to gain breathing patterns in cattle. Mask R-CNN was used to detect the ROI (region of interest, nose) in the cattle RGB images. Mask segmentation from the ROI detection was applied to the corresponding temperature data. Finally, to visualize the breathing pattern, we calculated the temperature values in the ROI by averaging all temperature values in the ROI. The results in this study show 76% accuracy with Mask R-CNN in detecting cattle noses. With respect to the temperature calculation methods, the averaging method showed the most appropriate breathing pattern compared to other methods (maximum temperature in the ROI and integrating all temperature values in the ROI). Finally, we compared the breathing pattern from the averaging method and that from the thermal image observation and found them to be highly correlated (R2 = 0.91). This method is not labor-intensive, can handle big data, and is accurate. In addition, we expect that the characteristics of the method might enable the analysis of temperature data from various angles. | |||||
| 言語 | en | |||||
| 内容記述 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | Citation: Kim S, Hidaka Y. Breathing Pattern Analysis in Cattle Using Infrared Thermography and Computer Vision. Animals (Basel). 2021 Jan 16;11(1):207. doi: 10.3390/ani11010207. PMID: 33466995; PMCID: PMC7830257. |
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| 言語 | en | |||||
| 書誌情報 |
en : Animals 巻 11, 号 1, p. 207, 発行日 2021-01-16 |
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| 出版者 | ||||||
| 出版者 | MDPI | |||||
| 言語 | en | |||||
| DOI | ||||||
| 関連タイプ | isVersionOf | |||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | https://doi.org/10.3390/ani11010207 | |||||
| 権利 | ||||||
| 言語 | en | |||||
| 権利情報 | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | |||||
| 著者版フラグ | ||||||
| 出版タイプ | VoR | |||||
