Xu, Beibei

Wang, Wensheng

Falzon, Gregory

Kwan, Paul

Guo, Leifeng

Chen, Guipeng

Tait, Amy

Schneider, Derek

2020

<p>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.</p>

Computers and Electronics in Agriculture, v.171, p. 1-12

1872-7107

0168-1699

https://hdl.handle.net/1959.11/58399

en

Elsevier BV

Attribution-NonCommercial-NoDerivatives 4.0 International

Automated cattle counting using Mask R-CNN in quadcopter vision system

Journal Article

Publication