Факультет інформатики

Permanent URI for this community

https://ekmair.ukma.edu.ua/handle/123456789/17456

Browse

Browsing Факультет інформатики by Author "Kurochkin, Andrew"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Comparative Analysis of Development Environments for UAV Software Development
    (2025) Budilova, Sofiia; Kurochkin, Andrew
    Unmanned aerial vehicles are in demand nowadays due to their ability to perform various tasks, both military and civilian, without the involvement of humans. The UAV simulators (UAV algorithm development environments) have a great value at the present day since they present a way to test all the new UAV algorithms that are spreading more and more finding real-world applications across various disciplines. A plethora of simulators already exist. All of them have their own advantages and disadvantages. This presents difficulties for developers to opt for the most suitable one to meet their requirements. This paper reviews the most popular UAV simulators. It also provides statistics regarding the amount of papers in general and throughout the years related to simulators and also to flight control software (e.g., ArduPilot). This provides the opportunity to observe tendencies in UAV simulation technologies. Subsequently, research was conducted to analyze scenes (usually called worlds) of the most popular and robust simulator, namely Gazebo, that are available open-source online. тA list of them is provided in this thesis. Eventually, a research gap was found, namely a shortage of Gazebo worlds containing moving objects. A new world with a moving car was created and might be used, for example, for the testing of the UAV object-tracking algorithms.
  • Thumbnail Image
    Item
    Development and Implementation of a Military Technology Trends Monitoring System
    (2025) Prokhorov, Oleksandr; Kurochkin, Andrew
    This work presents the design and implementation of a system for monitoring technological trends in the military sector using Telegram as a data source. The system automatically collects, processes, and analyzes both historical and real-time posts from selected Telegram channels, focusing on the emergence and dissemination of key terminology such as "реб" (eng.: "electronic warfare") in our evaluation case study. A modular architecture was developed, combining Go-based data scraping, Python-based aggregation and keyword analysis, and a Grafana dashboard for visualization. The system supports both local Docker-based deployment and cloud-based deployment via Terraform on AWS. Evaluation included performance benchmarks, peak resident-set size (RSS) profiling, and a case study comparing our system’s findings against professional media and Google Trends. Results indicate that a Telegram-based pipeline can detect rising interest in electronic-warfare topics earlier than traditional information channels.
  • Thumbnail Image
    Item
    Training YOLO Models for Real-Time Object Detection on UAV
    (2025) Solovei, Tymofii; Kurochkin, Andrew
    In this study, we have explored the implementation of the YOLOv8(nano) model for the task of real-time detection of military objects for UAV companion computers. We have collected and merged different datasets from the open sources with clearly annotated classes such as tanks, armored vehicles, armored personnel carriers, etc. Additionally, datasets with civilian people and vehicles have been included to address ethical concerns. Because of the poor quality of original datasets, we developed a processing pipeline for proper data selecting, filtering, and augmentation. We trained the YOLOv8-nano model for 100 epochs. The default pre-trained on the COCO dataset YOLOv8-nano model initially achieved an mAP@0.5 of 0.305 and mAP@0.5-0.95 of 0.169 on our dataset. Our final YOLOv8-nano model achieves a mAP@0.5 of 73.61% and mAP@0.5-0.95 of 51.18%. We also evaluated our model using combat videos from FPV(First Person View) drones containing different military targets.