文献类型： 外文期刊

作者： Xu, Beibei ¹ ; Wang, Wensheng ¹ ; Falzon, Greg ² ; Kwan, Paul ⁴ ; Guo, Leifeng ¹ ; Chen, Guipeng ⁵ ; Tait, Amy ⁶ ; Schnei ¹ ;

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

2.Univ New England, Sch Sci & Technol, Armidale, NSW 2351, Australia

3.Univ New England, Precis Agr Res Grp, Armidale, NSW 2351, Australia

4.Melbourne Inst Technol, Sch Informat Technol & Engn, Melbourne, Vic 3000, Australia

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

6.Univ New England, Sch Environm & Rural Sci, Armidale, NSW 2351, Australia

关键词： Object detection; Deep learning; Remote monitoring; Livestock management; Quadcopter vision system

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2020 年 171 卷

页码：

收录情况： SCI

摘要： The accurate and reliable counting of animals in quadcopter acquired imagery is one of the most promising but challenging tasks in intelligent livestock management in the future. In this paper we demonstrate the application of the cutting-edge instance segmentation framework, Mask R-CNN, in the context of cattle counting in different situations such as extensive production pastures and also in intensive housing such as feedlots. The optimal IoU threshold (0.5) and the full-appearance detection for the algorithm in this study are verified through performance evaluation. Experimental results in this research show the framework's potential to perform reliably in offline quadcopter vision systems with an accuracy of 94% in counting cattle on pastures and 92% in feedlots. Compared with the existing typical competing algorithms, Mask R-CNN outperforms both in the counting accuracy and average precision especially on the datasets with occlusion and overlapping. Our research shows promising steps towards the incorporation of artificial intelligence using quadcopters for enhanced management of animals.