Analysis of methods of object recognition and image compression during aerial photography from unmanned aerial vehicles

Abstract

In recent years, aerial photography from unmanned aerial vehicles (UAVs) has attracted considerable attention due to its wide range of applications, including mapping, surveillance and environmental monitoring. This scientific article is devoted to the analysis of methods of object recognition and image compression. Accurate real-time object recognition improves situational awareness, facilitating rapid decision-making and response in various areas. Image compression techniques provide efficient data storage and transmission, eliminating and improving bandwidth and storage capacity limitations in UAV systems. Optimizing these processes can significantly improve the overall productivity and efficiency of UAV aerial photography. The results of this study have implications in the military field, where UAVs are widely used for reconnaissance, surveillance, and target identification. Accurate object recognition techniques can improve military intelligence by enabling rapid identification of potential threats, identification of critical objects or targets of interest, and facilitating effective tactical planning. Real-time object recognition can help identify enemy vehicles, equipment and personnel, increasing situational awareness on the battlefield. In addition, effective image compression techniques can optimize the storage and transmission of aerial imagery, enabling rapid data sharing and analysis during military operations. This can optimize communication channels and facilitate timely decision-making in the field of military intelligence. This scientific article contains a comprehensive analysis of existing methods of object recognition and image compression, which can potentially be used for effective aerial reconnaissance with UAVs. By critically evaluating these methods, the paper aims at identifying their strengths, limitations and potential areas for improvement. The article presents a holistic view of the current state of affairs in this field, allowing researchers and practitioners to gain a deeper understanding of available methods and their potential applications in this field.

Keywords

• neural networks
• deep learning
• machine learning
• computer vision
• unmanned aerial vehicles (uavs)
• aerophotography