您好，欢迎访问江西省农业科学院 机构知识库！

CattleFaceNet: A cattle face identification approach based on RetinaFace and ArcFace loss

文献类型： 外文期刊

作者： Xu, Beibei 1 ; Wang, Wensheng 1 ; Guo, Leifeng 1 ; Chen, Guipeng 2 ; Li, Yongfeng 1 ; Cao, Zhen 1 ; Wu, Saisai 1 ;

作者机构： 1.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100086, Peoples R China

2.Jiangxi Acad Agr Sci, Agr Econ & Informat Inst, Nanchang 330200, Jiangxi, Peoples R China

3.Wageningen Univ & Res, Informat Technol Grp, NL-6708 PB Wageningen, Netherlands

关键词： Face recognition; RetinaFace; ArcFace loss; Deep learning; Precision livestock

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）

ISSN： 0168-1699

年卷期： 2022 年 193 卷

页码：

收录情况： SCI

摘要： Cattle identification is crucial to be registered for breeding association, food quality tracing, disease prevention and control and fake insurance claims. Traditional non-biometrics methods for cattle identification is not really satisfactory in providing reliability due to theft, fraud, and duplication. In this study, a computer vision technique was proposed to facilitate precision animal management and improve livestock welfare. This paper presents a novel face identification framework by integrating light-weight RetinaFace-mobilenet with Additive Angular Margin Loss (ArcFace), namely CattleFaceNet. RetinaFace-mobilenet is designed for face detection and location, and ArcFace is adopted to strengthen the within-class compactness and also between-class discrepancy during training. Experiments on real-word scenarios dataset prove that RetinaFace-mobilenet achieves superior detection performance and significantly accelerates the computation time against RetinaNet. Three loss functions utilized in human face recognition combined with RetinaFace-mobilenet are compared and results indict that the proposed CattleFaceNet outperforms others with identification accuracy of 91.3% and processing time of 24 frames per second (FPS). This research work demonstrates the potential candidate of CattleFaceNet for livestock identification in real time in practical production scenarios.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：江西省农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台