CONVOLUTIONAL NEURAL NETWORKS IN PROGRAMMING UNMANNED AERIAL VEHICLE - KOLOSOVA, KATERYNA; SICHKO, TETIANA; CHASTOKOLENKO, IHOR (Herald of Khmelnytskyi National University. Technical sciences, 2024)
The article examines the application of Convolutional Neural Networks (CNNs) in programming Unmanned Aerial Vehicles (UAVs) for efficient real-time object detection and tracking. The focus is on algorithms that ensure high accuracy in drone operations under challenging conditions, particularly in changing lighting and in the presence of obstacles. Modern CNN architectures are analyzed, along with their application in computer vision tasks such as object recognition, navigation, and autonomous drone control. Significant attention is given to the optimization of CNN models to match the limited computational resources of UAVs while maintaining high performance and processing speed. And emphasizes the importance of advanced algorithms that deliver high accuracy in drone operations, particularly when faced with challenging conditions such as varying lighting scenarios and the presence of obstacles in the environment.The paper also explores the possibilities of combining CNNs with other technologies, such as Recurrent Neural Networks (RNNs) or various types of sensors, to improve the accuracy and reliability of systems. It is shown that CNNs significantly enhance the autonomous capabilities of UAVs, enabling them to adapt to dynamic environments and quickly respond to changes. The article outlines prospects for further research, focusing on improving model efficiency and expanding the capabilities of UAVs in various operational contexts, such as security monitoring, environmental control, and search and rescue operations.In summary, this work makes a substantial contribution to the field of machine learning applications in aerial robotics. It underscores the pivotal role of CNNs in enhancing both the performance and autonomy of UAVs, ultimately paving the way for their broader utilization across various industries and sectors. The insights provided not only advance our understanding of drone technology but also open new horizons for innovative applications in the ever-evolving landscape of aerial operations.
Keywords: CNN; Unmanned Aerial Vehicles (UAVs); Object Detection; Object Tracking; Machine Vision; Real-Time; Image Processing