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

Beibei Xu, Wensheng Wang, Greg Falzon, Paul Kwan, Leifeng Guo, Guipeng Chen, Amy Tait, Derek Schneider

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number105300
Number of pages12
JournalCOMPUTERS AND ELECTRONICS IN AGRICULTURE
Volume171
DOIs
Publication statusPublished - Apr 2020
Externally publishedYes

Bibliographical note

© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

Keywords

  • Deep learning
  • Livestock management
  • Object detection
  • Quadcopter vision system
  • Remote monitoring

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