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<p>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 real-world dataset of drone-derived wildlife thermal detections. Wildlife detections, including arboreal (for instance, koalas, <i>Phascolarctos cinereus</i>) and ground dwelling species in our collected data are annotated via bounding boxes by experts. We then evaluate state-of-the-art object detection algorithms on our collected dataset and on a public dataset. We use these experimental results to identify issues and discuss future directions in automatic animal monitoring using drones.</p>

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An empirical study of automatic wildlife detection using drone-derived imagery and object detection

  • 期刊论文

  • 发表日期：

    2025年2月15日

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  • Tan Vuong,
  • Miao Chang,
  • Manas Palaparthi,
  • Lachlan G. Howell,
  • Alessio Bonti,
  • Mohamed Abdelrazek,
  • Duc Thanh Nguyen

摘要

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 real-world 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 evaluate state-of-the-art object detection algorithms on our collected dataset and on a public dataset. We use these experimental results to identify issues and discuss future directions in automatic animal monitoring using drones.