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This work contains a description of the results of modeling the process of maintaining the readiness of a section of the road network under strikes of with specified parameters. A one-dimensional section of road up to 40 km long with a total number of strikes up to 100 during the work of the brigade is considered. A simulation model has been developed for carrying out work to maintain it in working condition by several groups (engineering teams) that are part of the engineering and road division. A multicopter-type unmanned aerial vehicle is used to search for the points of appearance of obstacles. Life cycle schemes of the main participants of the tactical scene have been developed and an event-driven model of the tactical scene has been built. The format of the event log generated as a result of simulation modeling of the process of maintaining a road section is proposed. To visualize the process of maintaining the readiness of a road section, it is proposed to use visualization in the cyclogram format.

An XSL style has been developed for building a cyclogram based on an event log. As an algorithm for making a decision on the assignment of barriers to brigades, the simplest algorithm has been adopted, prescribing choosing the nearest barrier. A criterion describing the effectiveness of maintenance work on the site based on the assessment of the average speed of vehicles on the road section is proposed. Graphs of the dependence of the criterion value and the root-meansquare error depending on the length of the maintained section are plotted and an estimate is obtained for the maximum length of the road section maintained in a state of readiness with specified values for the selected quality indicator with specified characteristics of striking and performance of repair crews. The expediency of carrying out work to maintain readiness by several brigades that are part of the engineering and road division operating autonomously is shown.

The influence of the speed of the unmanned aerial vehicle on the ability to maintain the readiness of the road section is analyzed. The speed range for from 10 to 70 km/h is considered, which corresponds to the technical capabilities of multicoptertype reconnaissance unmanned aerial vehicles. The simulation results can be used as part of a complex simulation model of an army offensive or defensive operation and for solving the problem of optimizing the assignment of tasks to maintain the readiness of road sections to engineering and road brigades. The proposed approach may be of interest for the development of military-oriented strategy games.

The work analyzes known classes of convolutional neural network models and studies selected from them promising models for detecting flying objects in images. Object detection here refers to the detection, localization in space and classification of flying objects. The work conducts a comprehensive study of selected promising convolutional neural network models in order to identify the most effective ones from them for creating mobile real-time computer vision systems. It is shown that the most suitable models for detecting flying objects in images, taking into account the formulated requirements for mobile real-time computer vision systems, are models of the YOLO family, and five models from this family should be considered: YOLOv4, YOLOv4-Tiny, YOLOv4-CSP, YOLOv7 and YOLOv7-Tiny. An appropriate dataset has been developed for training, validation and comprehensive research of these models. Each labeled image of the dataset includes from one to several flying objects of four classes: “bird”, “aircraft-type unmanned aerial vehicle”, “helicopter-type unmanned aerial vehicle”, and “unknown object” (objects in airspace not included in the first three classes). Research has shown that all convolutional neural network models exceed the specified threshold value by the speed of detecting objects in the image, however, only the YOLOv4-CSP and YOLOv7 models partially satisfy the requirements of the accuracy of detection of flying objects. It was shown that most difficult object class to detect is the “bird” class. At the same time, it was revealed that the most effective model is YOLOv7, the YOLOv4-CSP model is in second place. Both models are recommended for use as part of a mobile real-time computer vision system with condition of additional training of these models on increased number of images with objects of the “bird” class so that they satisfy the requirement for the accuracy of detecting flying objects of each four classes.

The utilization of unmanned aerial vehicles (UAVs) in high-rise firefighting operations is the right solution for reaching the fire scene on high floors quickly and effectively. The article proposes a quadrotor-type firefighting UAV model carrying a launcher to launch a missile containing fire extinguishing powders into a fire. The kinematic model describing the flight kinematics of this UAV model is built based on the Newton – Euler method when the device is in normal motion and at the time of launching a firefighting missile. The results from the simulation testing the validity of the kinematic model and the simulation of the motion of the UAV show that the variation of Euler angles, flight angles, and aerodynamic angles during a flight are within an acceptable range and overload guarantee in flight. The UAV flew to the correct position to launch the required fire-extinguishing ammunition. The results of the research are the basis for building a control system of high-rise firefighting drones in Vietnam.

This article considers the task of multiclass classification of coniferous trees with varying degrees of damage by insect pests on images obtained using unmanned aerial vehicles (UAVs). We propose the use of convolutional neural networks (CNNs) for the classification of fir trees Abies sibirica and Siberian pine trees Pinus sibirica in unmanned aerial vehicles (UAV) imagery. In our approach, we develop three CNN models based on the classical U-Net architecture, designed for pixel-wise classification of images (semantic segmentation). The first model, Mo-U-Net, incorporates several changes to the classical U-Net model. The second and third models, MSC-U-Net and MSC-Res-U-Net, respectively, form ensembles of three Mo-U-Net models, each varying in depth and input image sizes. Additionally, the MSC-Res-U-Net model includes the integration of residual blocks. To validate our approach, we have created two datasets of UAV images depicting trees affected by pests, specifically Abies sibirica and Pinus sibirica, and trained the proposed three CNN models utilizing mIoULoss and Focal Loss as loss functions. Subsequent evaluation focused on the effectiveness of each trained model in classifying damaged trees. The results obtained indicate that when mIoULoss served as the loss function, the proposed models fell short of practical applicability in the forestry industry, failing to achieve classification accuracy above the threshold value of 0.5 for individual classes of both tree species according to the IoU metric. However, under Focal Loss, the MSC-Res-U-Net and Mo-U-Net models, in contrast to the third proposed model MSC-U-Net, exhibited high classification accuracy (surpassing the threshold value of 0.5) for all classes of Abies sibirica and Pinus sibirica trees. Thus, these results underscore the practical significance of the MSC-Res-U-Net and Mo-U-Net models for forestry professionals, enabling accurate classification and early detection of pest outbreaks in coniferous trees.