论文标题
基于深度学习的飞机检测的基准数据集：HRPLANES
A benchmark dataset for deep learning-based airplane detection: HRPlanes
论文作者
论文摘要
由于图像中的复杂背景以及由传感器几何形状和大气效应引起的数据采集条件的差异，卫星图像的飞机检测是一项艰巨的任务。深度学习方法为自动检测飞机提供了可靠，准确的解决方案；但是，需要大量培训数据才能获得有希望的结果。在这项研究中，我们通过使用Google Earth（GE）的图像并在图像上标记每个平面的边界框，创建一个称为高分辨率平面（HRPLANES）的新型飞机检测数据集。 HRPLANES包括世界各地几个不同机场的GE图像，以代表从不同卫星获得的各种景观，季节性和卫星几何形状条件。我们用两种广泛使用的对象检测方法（即Yolov4）和更快的R-CNN评估了数据集。我们的初步结果表明，所提出的数据集可以是用于未来应用程序的有价值的数据源和基准数据集。此外，提出的架构和这项研究的结果可用于转移不同数据集的学习和飞机检测模型。
Airplane detection from satellite imagery is a challenging task due to the complex backgrounds in the images and differences in data acquisition conditions caused by the sensor geometry and atmospheric effects. Deep learning methods provide reliable and accurate solutions for automatic detection of airplanes; however, huge amount of training data is required to obtain promising results. In this study, we create a novel airplane detection dataset called High Resolution Planes (HRPlanes) by using images from Google Earth (GE) and labeling the bounding box of each plane on the images. HRPlanes include GE images of several different airports across the world to represent a variety of landscape, seasonal and satellite geometry conditions obtained from different satellites. We evaluated our dataset with two widely used object detection methods namely YOLOv4 and Faster R-CNN. Our preliminary results show that the proposed dataset can be a valuable data source and benchmark data set for future applications. Moreover, proposed architectures and results of this study could be used for transfer learning of different datasets and models for airplane detection.
