An empirical study of automatic wildlife detection using drone thermal imaging and object detection
This work addresses wildlife management by providing a cost-effective method for monitoring species using drones, but it is incremental as it applies existing methods to new data.
The study tackled wildlife detection by benchmarking state-of-the-art object detection algorithms on a realistic drone thermal imaging dataset, achieving results that identify issues and discuss future directions for automatic animal monitoring.
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.