ドローン熱画像と物体検出による野生動物自動検出の実証研究
An empirical study of automatic wildlife detection using drone thermal imaging and object detection
ドローンによる熱画像から野生動物（コアラなど）を検出するための実データセットを構築し、最新の物体検出アルゴリズムを評価した研究。
著者: Miao Chang, Tan Vuong, Manas Palaparthi, Lachlan Howell, Alessio Bonti, Mohamed Abdelrazek, Duc Thanh Nguyen
分類: cs.CV, cs.MM, cs.RO
原文アブストラクト
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.