Real-Time Drone Data Processing for Aerial Photography Using Edge Computing and AI-Powered Algorithms

The rapid advancement of Unmanned Aerial Vehicles (UAVs) has made the field of aerial photography a dynamic, data-driven environment, requiring real-time data analysis and intelligent decision-making. However, the traditional cloud computing paradigm for processing UAV data has been found to be prone to latency, bandwidth constraints, and connectivity issues. This research is presented to address the problems in the traditional cloud computing paradigm for processing UAV data at a conceptual level, and it focuses on the integration of edge computing and AI-based algorithms for real-time data processing. This paper presents the use of edge computing and AI for real-time UAV data processing in aerial photography under obstacle constraints, emphasizing edge-AI, MDP and Q-learning concepts while providing a comparative study in evaluating various field-related examples. In the proposed framework, the intelligent decision-making for the UAV is achieved through the deployment of lightweight neural networks and computer vision models completely onboard or near-edge devices. This kind of system enables immediate object detection, scene classification, and adaptive flight control without entirely relying on continuous cloud connectivity. The proposed framework indicates the potential for improvements in processing speed, decision latency, and energy efficiency based on theoretical analysis and system design considerations. However, these benefits are not empirically validated in this study and are left for future experimental evaluation. The primary objective of this paper is to develop a unified edge-AI framework that enables real-time data processing and autonomous decision-making for UAVs operating in obstacle-constrained environments.