# An empirical study of automatic wildlife detection using drone thermal imaging and object detection Source: [An empirical study of automatic wildlife detection using drone thermal imaging and object detection]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:53:50.252Z Authors: Miao Chang, Tan Vuong, Manas Palaparthi, Lachlan Howell, Alessio Bonti, Mohamed Abdelrazek, Duc Thanh Nguyen Published: 2023-10-17T13:22:59.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()