Hangar Detection from Satellite Images with Mask-RCNN and YOLOv2 Algorithms

Hangar Detection from Satellite Images with Mask-RCNN and YOLOv2 Algorithms

Brilliant Engineering (BEN)
Volume 1 - Issue 2 - April 2020

Emin Argun Oral Nida Kumbasar Aslı Nur Ömeroğlu İbrahim Yücel Özbek

Abstract

This study proposes a new dataset of high resolution satellite images of hangars located at airports. It contains one thousand pictures obtained from Google Earth with different heights and angles. The detection of hangars is a challenging problem as the dataset contains camouflaged and non-camouflaged targets in different sizes. Mask R-CNN, regional based, and YOLOv2, regression based, algorithms were used in the detection problem. Mask R-CNN enables instance segmentation with a bounding box. YOLOv2, on the other hand, is used in real-time applications and provides only a bounding box. The object detection accuracy obtained by using Mask R-CNN and YOLOv2 algorithms to detect different sized objects was obtained as %72 and% 74, respectively.

Keywords

YOLOv2; Mask R-CNN; Satellite Images; Camouflaged and Non-Camouflaged Hangar; Object Detection
https://doi.org/10.36937/ben.2020.002.002