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Runway Detection and Localization in Aerial Images using Deep Learning

Landing is the most difficult phase of the flight for any airborne platform. Due to lack of efficient systems, there have been numerous landing accidents resulting in the damage of onboard hardware. Vision based systems provides low cost solution to detect landing sites by providing rich textual information. To this end, this research focuses on accurate detection and localization of runways in aerial images with untidy terrains which would consequently help aerial platforms especially Unmanned Aerial Vehicles (commonly referred to as Drones) to detect landing targets (i.e., runways) to aid automatic landing. Most of the prior work regarding runway detection is based on simple image processing algorithms with lot of assumptions and constraints about precise position of runway in a particular image. First part of this research is to develop runway detection algorithm based on state-of-the-art deep learning architectures while the second part is runway localization using both deep learning and non-deep learning based methods. The proposed runway detection approach is two-stage modular where in the first stage the aerial image classification is achieved to find the existence of runway in that particular image. Later, in the second stage, the identified runways are localized using both conventional line detection algorithms and more recent deep learning models. The runway classification has been achieved with an accuracy of around 97% whereas the runways have been localized with mean Intersection-over-Union (IoU) score of 0.8.