Enhancing house inspections: UAVs integrated with LLMs for efficient AI-powered surveillance

In the rapidly advancing landscape of technology, the integration of Unmanned Aerial Vehicles (UAVs) with Artificial Intelligence (AI) algorithms holds immense potential, particularly in the realm of house inspections. The burgeoning demand for innovative solutions in the surveillance domain has propelled researchers to explore transformative approaches. However, despite recent strides, significant challenges persist in seamlessly incorporating drones with the Internet of Things (IoT) networks, particularly in addressing response time concerns associated with intricate tasks such as facial recognition and motion detection. This research paper seeks to bridge these existing gaps by proposing an avant-garde architectural framework that directly deploys Large Language Models (LLMs) onto drones. The pivotal motivation stems from the need to not only enhance the performance of AI-enabled drones but also to overcome the limitations tied to centralized AI. While specific quantitative metrics in comparison to existing state-of-the-art methods are not available, this innovative approach demonstrates the potential for significant improvements in efficiency for localized tasks. This highlights the qualitative advancements achieved through our research. By delving into the intricacies of surveillance applications, this research not only contributes to the optimization of house inspections but also charts a path toward the development of more responsive, secure, and efficient AI-enabled drones, thereby shaping the future landscape of UAV and AI integration in surveillance scenarios.