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

Tolga Bakırman; Elif Sertel

Article Details

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

Authors:

Tolga Bakırman | Elif Sertel

Publisher:

Murat YAKAR

Journal:

International Journal of Engineering and Geosciences Vol.8 Issue 3, pp.212-223

Document Type:

Article

Publication Date:

10/2023

Document Language:

English

Document ID (Org No.):

2001289525

pISSN:

2548-0960

Keywords:

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.

 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.

 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.

 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.

Article Details

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

Authors: Tolga Bakırman | Elif Sertel

Publisher: Murat YAKAR

Journal: International Journal of Engineering and Geosciences Vol.8 Issue 3, pp.212-223

Document Type: Article

Publication Date: 10/2023

Document Language: English

Document ID (Org No.): 2001289525

pISSN: 2548-0960

Airplane detection

Deep learning

YOLO

Faster R-CNN

Google Earth

Issues

Authors:

Tolga Bakırman | Elif Sertel

Publisher:

Murat YAKAR

Journal:

International Journal of Engineering and Geosciences Vol.8 Issue 3, pp.212-223

Document Type:

Article

Publication Date:

10/2023

Document Language:

English

Document ID (Org No.):

2001289525

pISSN:

2548-0960