文献类型： 外文期刊
作者： Liu, Yanhong 1 ; Zhou, Fang 5 ; Zheng, Wenxin 1 ; Bai, Tao 1 ; Chen, Xinwen 6 ; Guo, Leifeng 1 ;
作者机构： 1.Xinjiang Agr Univ, Coll Comp & Informat Engn, Urumqi 830052, Peoples R China
2.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100080, Peoples R China
3.Xinjiang Agr Informatizat Engn Technol Res Ctr, Urumqi 830052, Peoples R China
4.Minist Educ, Engn Res Ctr Intelligent Agr, Urumqi 830052, Peoples R China
5.Shihezi Univ, Coll Informat Sci & Technol, Shihezi 832000, Peoples R China
6.Xinjiang Acad Anim Sci, Inst Anim Husb Qual Stand, Urumqi 830011, Peoples R China
7.Xinjiang Intelligent Livestock Key Lab, Urumqi 830052, Peoples R China
关键词： feeding; artificial intelligence; foal suckling; behavior recognition
期刊名称：ANIMALS （ 影响因子：2.7； 五年影响因子：3.2 ）
ISSN： 2076-2615
年卷期： 2025 年 15 卷 3 期
页码：
收录情况： SCI
摘要： Foal nursing behavior is a crucial indicator of healthy growth. The mare being in a standing posture and the foal being in a suckling posture are important markers for foal suckling behavior. To enable the recognition of a mare's standing posture and its foal's suckling posture in stalls, this paper proposes an RT-DETR-Foalnursing model based on RT-DETR. The model employs SACGNet as the backbone to enhance the efficiency of image feature extraction. Furthermore, by incorporating a multiscale multihead attention module and a channel attention module into the Adaptive Instance Feature Integration (AIFI), the model strengthens feature utilization and integration capabilities, thereby improving recognition accuracy. Experimental results demonstrate that the improved RT-DETR achieves a best mAP@50 of 98.5%, increasing by 1.8% compared to the RT-DETR. Additionally, this study achieves real-time statistical analysis of the duration of the foal in the suckling posture, which is one of the important indicators for determining whether the foal is suckling. This has significant implications for the healthy growth of foals.
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