数字农科院2.0

RFR-YOLO-Based Recognition Method for Dairy Cow Behavior in Farming Environments

文献类型： 外文期刊

作者： Congcong Li;Jialong Ma;Shifeng Cao;Leifeng Guo

作者机构：

关键词： cattle behavior recognition;multi-scale feature representation;object detection;YOLOv11n

期刊名称： Agriculture (Switzerland)

ISSN： 2077-0472

年卷期： 2025 年 15 卷 18 期

页码：

收录情况： SCIE（2025版）

摘要： Cow behavior recognition constitutes a fundamental element of effective cow health monitoring and intelligent farming systems. Within large-scale cow farming environments, several critical challenges persist, including the difficulty in accurately capturing behavioral feature information, substantial variations in multi-scale features, and high inter-class similarity among different cow behaviors. To address these limitations, this study introduces an enhanced target detection algorithm for cow behavior recognition, termed RFR-YOLO, which is developed upon the YOLOv11n framework. A well-structured dataset encompassing nine distinct cow behaviors—namely, lying, standing, walking, eating, drinking, licking, grooming, estrus, and limping—is constructed, comprising a total of 13,224 labeled samples. The proposed algorithm incorporates three major technical improvements: First, an Inverted Dilated Convolution module (Region Semantic Inverted Convolution, RsiConv) is designed and seamlessly integrated with the C3K2 module to form the C3K2_Rsi module, which effectively reduces computational overhead while enhancing feature representation. Second, a Four-branch Multi-scale Dilated Attention mechanism (Four Multi-Scale Dilated Attention, FMSDA) is incorporated into the network architecture, enabling the scale-specific features to align with the corresponding receptive fields, thereby improving the model’s capacity to capture multi-scale characteristics. Third, a Reparameterized Generalized Residual Feature Pyramid Network (Reparameterized Generalized Residual-FPN, RepGRFPN) is introduced as the Neck component, allowing for the features to propagate through differentiated pathways and enabling flexible control over multi-scale feature expression, thereby facilitating efficient feature fusion and mitigating the impact of behavioral similarity. The experimental results demonstrate that RFR-YOLO achieves precision, recall, mAP50, and mAP50:95 values of 95.9%, 91.2%, 94.9%, and 85.2%, respectively, representing performance gains of 5.5%, 5%, 5.6%, and 3.5% over the baseline model. Despite a marginal increase in computational complexity of 1.4G, the algorithm retains a high detection speed of 147.6 frames per second. The proposed RFR-YOLO algorithm significantly improves the accuracy and robustness of target detection in group cow farming scenarios.

分类号：

  • 相关文献

[1]Ag-YOLO: A Real-Time Low-Cost Detector for Precise Spraying With Case Study of Palms. Zhenwang Qin,Wensheng Wang,Karl Heinz Dammer,Leifeng Guo,Zhen Cao. 2021

[2]Noise-tolerant RGB-D feature fusion network for outdoor fruit detection. Qixin Sun,Xiujuan Chai,Zhikang Zeng,Guomin Zhou,Tan Sun. 2022

[3]A Deep Learning-Based Object Detection Scheme by Improving YOLOv5 for Sprouted Potatoes Datasets. Dai, Guowei,Hu, Lin,Fan, Jingchao,Yan, Shen,Li, Ruijing. 2022

[4]Rodent hole detection in a typical steppe ecosystem using UAS and deep learning. Du M.,Wang D.,Liu S.,Lv C.,Zhu Y.. 2022

[5]Convolutional Neural Network for Object Detection in Garlic Root Cutting Equipment. Yang, Ke,Peng, Baoliang,Gu, Fengwei,Zhang, Yanhua,Wang, Shenying,Yu, Zhaoyang,Hu, Zhichao. 2022

[6]Ag-YOLO: A Real-Time Low-Cost Detector for Precise Spraying With Case Study of Palms. Zhenwang Qin,Wensheng Wang,Karl Heinz Dammer,Leifeng Guo,Zhen Cao. 2022

[7]Automated cattle counting using Mask R-CNN in quadcopter vision system. 郭雷风,许贝贝. 2020

[8]Experimental Study of Garlic Root Cutting Based on Deep Learning Application in Food Primary Processing. Yang K.,Yu Z.,Gu F.,Zhang Y.,Wang S.,Peng B.,Hu Z.. 2022

[9]MRUNet: A two-stage segmentation model for small insect targets in complex environments. Fu kuan WANG,Yi qi HUANG,Zhao cheng HUANG,Hao SHEN,Cong HUANG,Xi QIAO,Wan qiang QIAN. 2023

[10]Locating Tea Bud Keypoints by Keypoint Detection Method Based on Convolutional Neural Network. Yifan Cheng,Yang Li,Rentian Zhang,Zhiyong Gui,Chunwang Dong,Rong Ma. 2023

[11]Mapping Maize Planting Densities Using Unmanned Aerial Vehicles, Multispectral Remote Sensing, and Deep Learning Technology. Jianing Shen,Qilei Wang,Meng Zhao,Jingyu Hu,Jian Wang,Meiyan Shu,Yang Liu,Wei Guo,Hongbo Qiao,Qinglin Niu,Jibo Yue. 2024

[12]Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review. Zhen Cao,Lammert Kooistra,Wensheng Wang,Leifeng Guo,João Valente. 2023

[13]A Novel Deep Learning Method for Detecting Strawberry Fruit. Shuo Shen,Famin Duan,Zhiwei Tian,Chunxiao Han. 2024

[14]Small target tea bud detection based on improved YOLOv5 in complex background. Mengjie Wang,Yang Li,Hewei Meng,Zhiwei Chen,Zhiyong Gui,Yaping Li,Chunwang Dong. 2024

[15]UAV-based field watermelon detection and counting using YOLOv8s with image panorama stitching and overlap partitioning. Liguo Jiang,Hanhui Jiang,Xudong Jing,Haojie Dang,Rui Li,Jinyong Chen,Yaqoob Majeed,Ramesh Sahni,Longsheng Fu. 2024

[16]Leaf rolling detection in maize under complex environments using an improved deep learning method. Wang Y.,Jing X.,Gao Y.,Han X.,Zhao C.,Pan W.. 2024

[17]Comparison and Optimal Method of Detecting the Number of Maize Seedlings Based on Deep Learning. Zhijie Jia,Xinlong Zhang,Hongye Yang,Yuan Lu,Jiale Liu,Xun Yu,Dayun Feng,Kexin Gao,Jianfu Xue,Bo Ming,Chenwei Nie,Shaokun Li. 2024

[18]Automatic detection and counting of planthoppers on white flat plate images captured by AR glasses for planthopper field survey. Haiyuan Sheng,Qing Yao,Ju Luo,Yongjian Liu,Xiangfu Chen,Zhongru Ye,Tiezhuang Zhao,Heping Ling,Jian Tang,Shuhua Liu. 2024

[19]Melon ripeness detection by an improved object detection algorithm for resource constrained environments. Xuebin Jing,Yuanhao Wang,Dongxi Li,Weihua Pan. 2024

[20]YOLOv5DA: An Improved YOLOv5 Model for Posture Detection of Grouped Pigs. Wenhui Shi,Xiaopin Wang,Xuan Li,Yuhua Fu,Xiaolei Liu,Haiyan Wang. 2025

作者其他论文 更多>>