A benchmark dataset for deep learning-based airplane detection: HRPlanes

IF 3.1 Q2 ENGINEERING, GEOLOGICAL
Tolga BAKIRMAN, Elif SERTEL
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引用次数: 1

Abstract

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.
基于深度学习的飞机检测基准数据集:HRPlanes
由于卫星图像背景复杂,加之传感器几何形状和大气效应导致的数据采集条件差异,从卫星图像中探测飞机是一项具有挑战性的任务。深度学习方法为飞机自动检测提供可靠、准确的解决方案;然而,要获得令人满意的结果,需要大量的训练数据。在本研究中,我们通过使用来自Google Earth (GE)的图像并在图像上标记每个飞机的边界框,创建了一个名为High Resolution Planes (HRPlanes)的新型飞机检测数据集。HRPlanes包括全球几个不同机场的GE图像,以表示从不同卫星获得的各种景观,季节和卫星几何条件。我们使用两种广泛使用的目标检测方法(即YOLOv4和Faster R-CNN)来评估我们的数据集。我们的初步结果表明,所提出的数据集可以成为未来应用的有价值的数据源和基准数据集。此外,本研究提出的架构和结果可用于飞机检测的不同数据集和模型的迁移学习。
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来源期刊
CiteScore
4.00
自引率
0.00%
发文量
12
审稿时长
30 weeks
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