Akademik Veri Yönetim Sistemi
Brilliant Engineering (BEN), cilt.1, sa.2, ss.5-9, 2019 (Hakemli Dergi)
The contribution of this paper is twofold. It first proposes a new dataset of high resolution satellite images of
hangars located at civil and military airports. It also presents a hangar detection problem results from satellite
images using this new dataset obtained by Mask R-CNN and YOLOv2 algorithms. The satellite dataset contains
one thousand pictures obtained from Google Earth at 8, 11, 17, 29 degrees angles from height of 500,800 and
1000 meters. Among 1000 hangar images 650 and 200 images are used for training and validation, respectively,
while the remaining 150 images are used for detection test purposes. The total number of hangar object instants
in the dataset images is about 3000. The detection of hangars is a challenging problem as the dataset contains
camouflaged and non-camouflaged targets in different sizes. Among the two approaches used in the detection
problem Mask R-CNN utilizes a regional based algorithm and enables instance segmentation with a bounding
box. YOLOv2, on the other hand, is a regression based algorithm, used in real-time applications, and provides a
bounding box only. The object detection accuracy in terms of Average Precisions by using Mask R-CNN and
YOLOv2 algorithms to detect different sized camouflaged and non-camouflaged hangar objects was obtained as
72% and 74%, respectively.