光学・赤外カメラ搭載UAVのための深層学習による人検出システム
Deep Learning-based Human Detection for UAVs with Optical and Infrared Cameras: System and Experiments
UAVに搭載したRGB・赤外カメラのRetinaNet検出を融合し、高高度からの人検出・追跡・再同定を行うシステムを構築、検出漏れを20%以上削減した。
著者: Timo Hinzmann, Tobias Stegemann, Cesar Cadena, Roland Siegwart
分類: cs.CV, cs.RO
原文アブストラクト
In this paper, we present our deep learning-based human detection system that uses optical (RGB) and long-wave infrared (LWIR) cameras to detect, track, localize, and re-identify humans from UAVs flying at high altitude. In each spectrum, a customized RetinaNet network with ResNet backbone provides human detections which are subsequently fused to minimize the overall false detection rate. We show that by optimizing the bounding box anchors and augmenting the image resolution the number of missed detections from high altitudes can be decreased by over 20 percent. Our proposed network is compared to different RetinaNet and YOLO variants, and to a classical optical-infrared human detection framework that uses hand-crafted features. Furthermore, along with the publication of this paper, we release a collection of annotated optical-infrared datasets recorded with different UAVs during search-and-rescue field tests and the source code of the implemented annotation tool.
