An empirical study of automatic wildlife detection using drone thermal
  imaging and object detection
Miao Chang Tan Vuong Manas Palaparthi Lachlan Howell Alessio Bonti Mohamed Abdelrazek Duc Thanh Nguyen
Artificial intelligence has the potential to make valuable contributions to
wildlife management through cost-effective methods for the collection and
interpretation of wildlife data. Recent advances in remotely piloted aircraft
systems (RPAS or ``drones'') and thermal imaging technology have created new
approaches to collect wildlife data. These emerging technologies could provide
promising alternatives to standard labourious field techniques as well as cover
much larger areas. In this study, we conduct a comprehensive review and
empirical study of drone-based wildlife detection. Specifically, we collect a
realistic dataset of drone-derived wildlife thermal detections. Wildlife
detections, including arboreal (for instance, koalas, phascolarctos cinereus)
and ground dwelling species in our collected data are annotated via bounding
boxes by experts. We then benchmark state-of-the-art object detection
algorithms on our collected dataset. We use these experimental results to
identify issues and discuss future directions in automatic animal monitoring
using drones.