Aerial Animal Biometrics: Individual Friesian Cattle Recovery and Visual Identification via an Autonomous UAV with Onboard Deep Inference

Will Andrew, Colin Greatwood, Tilo Burghardt

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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Abstract

This paper describes a computationally-enhanced M100 UAV platform with an onboard deep learning inference system for integrated computer vision and navigation. The system is able to autonomously find and visually identify by coat pattern individual Holstein Friesian cattle in freely moving herds. We propose an approach that utilises three deep convolutional neural network architectures running live onboard the aircraft:
(1) a YOLOv2-based species detector,
(2) a dual-stream deep network delivering exploratory agency, and
(3) an InceptionV3-based biometric long-term recurrent convolutional network for individual animal identification.
We evaluate the performance of each of the components offline, and also online via real-world field tests comprising 147 minutes of autonomous low altitude flight in a farm environment over a dispersed herd of 17 heifer dairy cows. We report error free identification performance on this online experiment. The presented proof-of-concept system is the first of its kind. It represents a practical step towards autonomous biometric identification of individual animals from the air in open pasture environments for tag-less AI support in farming and ecology.
Original languageEnglish
Title of host publication2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages7
ISBN (Electronic)978-1-7281-4004-9
DOIs
Publication statusPublished - 27 Jan 2020
EventIEEE/RSJ International Conference on Intelligent Robots and Systems - The Venetian Macao, Macau, China
Duration: 3 Nov 20198 Nov 2019
https://www.iros2019.org

Publication series

NameIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherIEEE
ISSN (Electronic)2153-0866

Conference

ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleiROS 2019
CountryChina
CityMacau
Period3/11/198/11/19
Internet address

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  • Student Theses

    Visual Biometric Processes for Collective Identification of Individual Friesian Cattle

    Author: Andrew, W., 7 May 2019

    Supervisor: Burghardt, T. (Supervisor), Greatwood, C. (Supervisor) & Richards, A. (Supervisor)

    Student thesis: Doctoral ThesisDoctor of Philosophy (PhD)

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    Cite this

    Andrew, W., Greatwood, C., & Burghardt, T. (2020). Aerial Animal Biometrics: Individual Friesian Cattle Recovery and Visual Identification via an Autonomous UAV with Onboard Deep Inference. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IROS40897.2019.8968555