Performance Analysis of AeroShield: A Night Vision Drone for Security and Defense Applications

Abstract Unmanned Aerial Vehicles (UAVs) have emerged as vital tools in modern surveillance and defense systems, enabling persistent monitoring and rapid response across diverse terrains. This paper presents AeroShield, an advanced night vision-enabled drone designed to enhance aerial security in low-light and no-light environments. The AeroShield platform integrates infrared (IR) imaging sensors, high-resolution thermal cameras, and AI-based image enhancement algorithms to ensure reliable object detection and tracking under challenging visibility conditions. Unlike conventional UAVs, AeroShield leverages onboard processing for real-time image classification and anomaly detection, thereby improving situational awareness during night operations. The system is optimized for endurance, lightweight structural design, and adaptive flight control to minimize energy consumption while maintaining operational efficiency. Performance analysis is conducted through simulated and real-world test scenarios, evaluating parameters such as detection accuracy, flight stability, battery efficiency, and communication latency. The results demonstrate AeroShield’s superior performance in surveillance, border monitoring, disaster response, and defense applications, particularly in environments where human intervention is limited or unsafe. This study highlights AeroShield as a cost-effective, scalable, and reliable UAV framework, contributing significantly to next-generation security technologies.