Adapting astronomical source detection software to help detect animals in thermal images obtained by unmanned aerial systems

ABSTRACT In this article, we describe an unmanned aerial system equipped with a thermal-infrared camera and software pipeline that we have developed to monitor animal populations for conservation purposes. Taking a multi-disciplinary approach to tackle this problem, we use freely available astronomical source detection software and the associated expertise of astronomers, to efficiently and reliably detect humans and animals in aerial thermal-infrared footage. Combining this astronomical detection software with existing machine learning algorithms into a single, automated, end-to-end pipeline, we test the software using aerial video footage taken in a controlled, field-like environment. We demonstrate that the pipeline works reliably and describe how it can be used to estimate the completeness of different observational datasets to objects of a given type as a function of height, observing conditions, etc. – a crucial step in converting video footage to scientifically useful information such as the spatial distribution and density of different animal species. Finally, having demonstrated the potential utility of the system, we describe the steps we are taking to adapt the system for work in the field, in particular systematic monitoring of endangered species at National Parks around the world.

[1]  S. Buckland.  Introduction to distance sampling : estimating abundance of biological populations , 2001 .

[2]  Jérôme Théau,et al.  WILDLIFE MULTISPECIES REMOTE SENSING USING VISIBLE AND THERMAL INFRARED IMAGERY ACQUIRED FROM AN UNMANNED AERIAL VEHICLE (UAV) , 2015 .

[3]  S. Wich,et al.  Dawn of Drone Ecology: Low-Cost Autonomous Aerial Vehicles for Conservation , 2012 .

[4]  Prasanth H. Nair,et al.  Astropy: A community Python package for astronomy , 2013, 1307.6212.

[5]  J. Vicente,et al.  Unmanned Aircraft Systems complement biologging in spatial ecology studies , 2015, Ecology and evolution.

[6]  Measuring the influence of unmanned aerial vehicles on Adélie penguins , 2016, Polar Biology.

[7]  A. Skidmore,et al.  Erasmus Mundus - External cooperation window as a framework for higher education cooperation in the middle east region: opportunities and challenges , 2010 .

[8]  Julie Linchant,et al.  Are unmanned aircraft systems (UASs) the future of wildlife monitoring? A review of accomplishments and challenges , 2015 .

[9]  David R. Anderson,et al.  Distance Sampling: Estimating Abundance of Biological Populations , 1995 .

[10]  Paul A. Iaizzo,et al.  Bears Show a Physiological but Limited Behavioral Response to Unmanned Aerial Vehicles , 2015, Current Biology.

[11]  David R. Anderson,et al.  Advanced Distance Sampling: Estimating Abundance of Biological Populations , 2004 .

[12]  Db Sasse,et al.  Job-Related Mortality of Wildlife Workers in the United States, 1937-2000 , 2003 .

[13]  F. Sergio,et al.  Decoration Increases the Conspicuousness of Raptor Nests , 2016, PloS one.

[14]  Sandra Johnson,et al.  Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation , 2016, Sensors.

[15]  Lian Pin Koh,et al.  Locating chimpanzee nests and identifying fruiting trees with an unmanned aerial vehicle , 2015, American journal of primatology.

[16]  Martin Israel.  A UAV-BASED ROE DEER FAWN DETECTION SYSTEM , 2012 .

[17]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[18]  Airam Rodríguez,et al.  The Eye in the Sky: Combined Use of Unmanned Aerial Systems and GPS Data Loggers for Ecological Research and Conservation of Small Birds , 2012, PloS one.

[19]  Julie Linchant,et al.  HOW MANY HIPPOS (HOMHIP): ALGORITHM FOR AUTOMATIC COUNTS OF ANIMALS WITH INFRA-RED THERMAL IMAGERY FROM UAV , 2015 .

[20]  D. Bird,et al.  Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in?1 , 2015 .

[21]  M. Mulero-Pázmány,et al.  Remotely Piloted Aircraft Systems as a Rhinoceros Anti-Poaching Tool in Africa , 2014, PloS one.

[22]  Alan Hobbs,et al.  Unmanned Aircraft Systems , 2010 .

[23]  Zheng Yang,et al.  Spotting East African Mammals in Open Savannah from Space , 2014, PloS one.

[24]  GEORGE PIERCE JONES,et al.  An Assessment of Small Unmanned Aerial Vehicles for Wildlife Research , 2006 .

[25]  Jérôme Théau,et al.  Visible and thermal infrared remote sensing for the detection of white‐tailed deer using an unmanned aerial system , 2016 .

[26]  C. Gortázar,et al.  Unmanned Aircraft Systems for Studying Spatial Abundance of Ungulates: Relevance to Spatial Epidemiology , 2014, PloS one.

[27]  Peter T. Fretwell,et al.  Whales from Space: Counting Southern Right Whales by Satellite , 2014, PloS one.

[28]  L. P. Koh,et al.  Best practice for minimising unmanned aerial vehicle disturbance to wildlife in biological field research , 2016, Current Biology.

[29]  Clive R. McMahon,et al.  Satellites, the All-Seeing Eyes in the Sky: Counting Elephant Seals from Space , 2014, PloS one.

[30]  Gerald L. Kooyman,et al.  An Emperor Penguin Population Estimate: The First Global, Synoptic Survey of a Species from Space , 2012, PloS one.