Segmentation of image from a first-person-view unmanned aerial vehicle based on a simple ant algorithm

The object of this study is the process of image segmentation from the First Person View (FPV) of an unmanned aerial vehicle (UAV). The main hypothesis of the study assumes that the use of a simple ant algorithm could ensure the necessary quality of the segmented image. The segmentation method, unlike the known ones, takes into account the number of ants in the image, weight, initial amount and evaporation rate of the pheromone, the "greediness" of the algorithm and provides: – preliminary selection of individual channels of the Red-Green-Blue (RGB) color space; – preliminary placement of ants according to the uniform law; – determining the routes of ants; – taking into account the attractiveness of the route for each ant; – change (adjustment) in the concentration of ant pheromones; – calculation of the probability of movement (transition) of the ant on the movement route; – determination of the objective function at the j-th iteration and its minimization; – determining the coordinates of the route of movement (movement) of ants; – verification of the fulfillment of the stop condition; – determination of the best routes found by ants; – calculation of the brightness of the pixels of the segmented image in each channel of the RGB color space; – further combining the results of channel segmentation. An experimental study of image segmentation from UAV FPV based on a simple ant algorithm was conducted. The specified object of interest on the segmented image has a certain structure, unevenness of the contours, and can be further used for decoding, categorization, etc. Unlike the object of interest, the background ("garbage" objects) in the segmented image do not have a stable structure and can be further filtered out. It has been established that the segmented image by the known method based on the gradient module has a low contrast value, there are gaps in the segmented pixels of the object of interest. A segmented image using a method based on a simple ant algorithm is free from that drawback.