| Item type |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2026-01-07 |
| タイトル |
|
|
タイトル |
Advanced Dairy Cow Monitoring : Enhanced Detection with Precision 3D Tracking |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Dairy cow monitoring |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Multi-target detection |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Multi-target tracking |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
YOLOv5 |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
DeepSORT |
| 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 |
departmental bulletin paper |
| ID登録 |
|
|
ID登録 |
10.15113/0002001335 |
|
ID登録タイプ |
JaLC |
| 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 |
WANG, Ranran
LI, Yingxiu
YUE, Peng
YUAN, Chunhong
TIAN, Fuyang
LU, Xin
|
| 登録日 |
|
|
日付 |
2026-01-07 |
|
日付タイプ |
Created |
| 書誌情報 |
en : Multimedia Tools and Applications
巻 84,
p. 19117-19146,
ページ数 30,
発行日 2024-07-13
|
| ISSN |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
1573-7721 |
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Ensuring the welfare of dairy cows requires precise monitoring of their daily exercise to evaluate their physical health. This necessitates innovative methods beyond traditional motion sensors. We present a novel method that integrates an enhanced YOLOv5s object detection model with the DeepSORT multi-object tracking algorithm to meticulously track dairy cow movements, providing holistic information about their health. Our research started with the establishment of a dedicated dataset tailored for cow detection. We then segmented the detection scope to focus on specific regions of interest. Within the modified YOLOv5s model, we replaced the standard CSPDarknet53 backbone with DenseNet to achieve deep separable convolution and feature reorganization modules, leading to reduced parameters, augmented feature expression, and better information flow. In particular, the SPD-Conv module was incorporated to retain intricate details, essential for detecting smaller and low-resolution targets. The transition from Generalized Intersection over Union (GIoU) Loss to Complete Intersection over Union (CIoU) loss improved detection accuracy and sped up model convergence. Our clustering approach, based on the elbow rule, optimized K-means clustering, enhancing speed and precision. For multi-object tracking, the DeepSORT model was tailored to cater to varying cow sizes, and we chose an algorithm to associate appearance information. We converted pixel data into real-world distances, providing exact 3D cow movement metrics. Experimental validation confirmed the efficacy of our approach. Our enhanced model surpassed the original YOLOv5s in performance by 11.1% for accuracy (97.4%), 9.6% for recall (97.8%), and 11.0% for average accuracy (98.2%). The comprehensive accuracy stood at 92.1% for our model. In conclusion, our innovative methodology offers a non-invasive means to monitor dairy cow exercise, paving the way for advanced health assessment techniques in the dairy sector. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
Springer Nature |
|
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/10.1007/s11042-024-19791-8 |
| 著者版フラグ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| 助成情報 |
|
|
|
プログラム情報 |
National Key Technologies Research and Development Program of China, Subproject |
|
|
研究課題番号 |
2023YFD2000704-2 |
|
|
研究課題名 |
Development of an Intelligent Inspection Robot for Health Assessment in Beef Cattle Factory Farming |
|
|
言語 |
en |
| 内容注記 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
This work was supported by the National Key Technologies Research and Development Program of China, Subproject: 'Development of an Intelligent Inspection Robot for Health Assessment in Beef Cattle Factory Farming' (Project No. 2023YFD2000704-2). |
|
言語 |
en |
| 内容注記 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11042-024-19791-8 |
|
言語 |
en |
mta-v84p19117-19146.pdf (6 MB)
