The article focuses on the stage of determining information zones on images from on-board surveillance systems in order to provide information for their classification, necessary for further semantic segmentation and recognition. Information zones in the study are understood as zones where objects of interest are most likely to be found. Objects of interest can be elements of urban infrastructure (roads, streets, buildings, etc.), railway stations and tracks, dividing lines between different texture objects, objects (cars, equipment, military facilities, etc.). The main motivation for the study is the allocation of such information zones in order to reduce the time for image processing (improving the efficiency of image processing). The method for determining information zones on images of on-board surveillance systems has been improved. It consists of the following steps: processing the original image with Canny edge detector and using the Hough transform on the segmented image. The results of the work of this method are presented on the color image of the space-based surveillance systems and on the color image from an unmanned aerial vehicle. Visual assessment of the quality of the proposed method is satisfactory. A quantitative indicator of the quality of the method for determining information zones on images of on-board surveillance systems was calculated. Analysis of the quantitative indicator showed that this method for determining information zones on images of on-board surveillance systems makes it possible to increase the efficiency of further image processing by the operator interpreter for further semantic segmentation and recognition images.

The paper proposes the comparative evaluation of sequential and parallel methods for identifying measurements of nearby objects. The problem statement is choosing the method for identifying measurements of nearby objects. The decision rule for identifying the results of radio engineering measurements of the coordinates of objects is obtained. The decision rules for identifying objects by sequential and parallel methods under various conditions are obtained. The cases of absence and presence of false marks from nearby objects are considered. The task of choosing serial or parallel methods for identifying measurements from nearby objects was set. The comparative evaluation of serial and parallel measurement identification methods is provided. As an indicator of the effectiveness of the methods, we chosen the probability of identification error. We estimated the probability of identification error depending on the relative average distance between objects in the absence of false measurements and in their presence. It is determined that the probability of an identification error when using the parallel identification method is less than when using the sequential identification method. This gain increases as the average relative distance between objects decreases. Based on the analysis of methods for identifying measurement results, it was found that the most appropriate identification method is a parallel method for identifying measurement results. The efficiency of the method increases with decreasing relative average distance between objects. Keywords— evaluation, sequential method, parallel Method, identification, measurement, nearby object, hypothesis