论文标题
具有光学和红外摄像机无人机的深度学习人类检测：系统和实验
Deep Learning-based Human Detection for UAVs with Optical and Infrared Cameras: System and Experiments
论文作者
论文摘要
在本文中，我们介绍了使用光学（RGB）和长波红外（LWIR）摄像机来检测，跟踪，本地化和重新识别在高空飞行的无人机飞行中的人类的人类摄像机。在每个频谱中，具有重新网络骨架的定制视网膜网络提供了人类检测，随后融合以最大程度地减少整体错误检测率。我们表明，通过优化边界框锚并增强图像分辨率，高海拔的遗漏检测数量可以减少20％以上。将我们提出的网络与不同的视网膜和Yolo变体进行了比较，以及使用手工制作的特征的经典光红外人类检测框架。此外，随着本文的出版，我们发布了在搜索和响应现场测试期间与不同无人机录制的注释的光 - 信号数据集的集合以及已实现的注释工具的源代码。
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.
