Segmentation of Images Used in Unmanned Aerial Vehicles Navigation Systems

Abstract

The paper presents the results of the study of a two-stage procedure for selecting a reference object in the current image formed by a correlation-extreme system used for autonomous navigation of unmanned aerial vehicles. The aim of this paper is to theoretically evaluate the probability of selecting low-dimensional low-contrast objects in the segmented current image according to the proposed two-stage procedure. To achieve this goal, the problem of segmentation of images of the sighting surface and subsequent selection of the reference object in the presence of heterogeneous objects differing in brightness and area characteristics is solved. The most significant result is the justification of application of two-stage procedure of selection of the reference object in the current image by brightness and area parameters using the set thresholds. The significance of the obtained results consists in establishing the dependence of the probability of correct selection of the reference object on the noise level of the current images. It is shown that the probability of correct selection of the object in the image is a function of the threshold value and can be maximised by choosing its value. This approach allows to consider the influence of various factors leading to image noise on the quality of images formed by the navigation system. It is shown that when noise distorts more than 31% of the image pixels, the proposed two-stage procedure allows to ensure the selection of the reference object in the image with a probability not lower than 0.9.

Keywords

• correlation-extremal navigation system
• unmanned aerial vehicles
• information features
• image segmentation method
• decision function.