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

Authors : Tolga BAKIRMAN, Elif SERTEL

Pages : 212-223

Doi:10.26833/ijeg.1107890

View : 273 | Download : 235

Publication Date : 2023-10-15

Article Type : Research Paper

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 insert ignore into journalissuearticles values(HRPlanes); by using images from Google Earth insert ignore into journalissuearticles values(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.
Keywords : Airplane detection, Deep learning, YOLO, Faster R CNN, Google Earth