Divide and conquer object detection (DACOD) method for runway detection in remote sensing images

Abstract

In recent years, parallel to the developments in satellite technology, obtaining and processing remote sensing images has become quite common. While airports are the first points to be targeted by enemy forces in times of war, they are very critical points in times of peace due to their significance for transportation, trade, and economy networks. The runways are the most distinctive feature of airports. There are many studies on detecting the runways in remote sensing images (RSIs). However, existing methods for detecting the runway objects that have an excessive width in high-resolution (4137 x 4552 pixels and above) RSIs may be insufficient. In this study, a Divide and Conquer Object Detection (DACOD) method is proposed for the runway objects that have an excessive width in high-resolution RSIs. In the proposed method, images are divided into images of 1024 x 1024 pixels, and the runway objects in these images are detected as oriented. Then, the detection results are merged by using the angles and the final runway detection results are obtained. The experimental results demonstrate that the proposed model yields good results (%81.5 mAP). This is an 11% mAP increase when compared to the best results in The State of The Art (SOTA) object detection models using the same dataset.

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

“…[3]. Airplane detection is essential in various fields such as airport surveillance [4], transportation activity analysis [5], defence and military applications and satellite imagery is a significant data source for this purpose with the advantages of covering large areas very quickly and periodically [6].…”